{"access":{"catalog_url":"https://aidevboard.com/api/v1/catalog","description":"Public read endpoints are open and free. 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We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the role \n Anthropic's Safeguards organization builds the policies, evaluations, and detection and enforcement systems that define and hold the limits on how Claude can be used. In this role, you'll lead our policy design team, managing the teams responsible for radicalization, child safety, user well-being, harmful manipulation, and election integrity, among other harm areas.\n The team is responsible for understanding and defining the risks that come with engaging with Claude, how those risks materialize in the real world, and the mitigations needed to prevent them. As the manager, you'll work with your team to draw the boundaries between what is and is not allowed, then partner with research, product, and engineering to build the right interventions. Mitigating these harms takes the whole stack: the values and judgment trained into the model itself, the policies and detection systems we enforce on top of it, and the interventions we build into our products. More capable models, new product surfaces, and new user behaviors will keep testing these boundaries, so the team's policies have to keep pace.\n You'll work closely with product to develop and iterate on the strategy and vision for how our safety layers fit together, and with your team and cross-functional partners to decide which mitigations make the most sense and how to implement them. You'll also coordinate policy decisions across the portfolio: ensuring they're made with the right stakeholders in the room, tracked over time, and applied consistently across harm areas and product surfaces.\n This is a leadership role for someone who combines expertise in the harm areas themselves with fluency in how frontier models are actually developed and deployed, and who does their best work across team boundaries. You'll spend as much time developing the leads who own each harm area as you will on the policy questions themselves.\n *Important context for this role: some of the work involves exposure to explicit content, including material of a sexual, violent, or psychologically disturbing nature.\n Key responsibilities \n \n \n Lead, develop, and grow the managers and teams responsible for the consumer harms portfolio, including child safety, user well-being, harmful manipulation, and election integrity\n \n Coordinate policy decisions across the portfolio, and build the mechanisms that keep them tracked, consistent, and legible — so stakeholders know what was decided, why, and who owns what\n \n Set the strategy for how mitigations built on top of the model — policies, detection and enforcement systems, and product interventions — complement what is trained into the model itself, partnering closely with the alignment training team that owns Claude's character\n \n Prioritize across harm areas competing for the same resources, and make those tradeoffs and their rationale clear to leadership\n \n Serve as the escalation point for high-severity and ambiguous consumer harms decisions, including rapid response to emerging risks\n \n Partner with engineering, data science, product, legal, and research across the model development cycle so consumer harms considerations are represented from training through launch, on every surface where Claude is deployed\n \n Engage external experts, civil society organizations, and regulators, and translate that engagement into stronger policy and enforcement\n \n Minimum qualifications \n \n \n Experience leading teams — including managing managers or senior specialists — in AI safety, product policy, or a related field\n \n Deep, applied familiarity with consumer harm areas such as child safety, mental health and well-being, manipulation, or election integrity, and good judgment about how these harms differ in mechanism, severity, and mitigation\n \n A track record of exceptional cross-team collaboration: building durable working relationships with teams you don't control, and getting to shared decisions where ownership is genuinely distributed\n \n Working understanding of how frontier models are developed and deployed — the training and fine-tuning cycle, evaluations, and launch processes — and how different model environments (consumer products, APIs, agentic tools) change both risk and the mitigations available\n \n Experience translating policy positions into mechanisms that can be enforced and measured, and communicating the reasoning to technical and non-technical audiences, including executives\n \n Sound judgment in ambiguous, high-consequence decisions, and comfort making a call and escalating appropriately on incomplete information\n \n Preferred qualifications \n \n \n Subject-matter depth in one","salary_min":330000,"salary_max":395000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["generative-ai","agents","llm","alignment","fine-tuning","healthcare","rust"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5407418008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-28T19:56:10Z","expires_at":"2026-09-29T13:30:21.361128Z","created_at":"2026-08-29T13:30:22.347939Z","updated_at":"2026-08-30T13:30:21.508142Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/8f5683c8-4601-44f3-ae28-99878029e10f"},{"id":"57b56d5b-e1f9-4118-afb8-2bd1f37d7f46","company_id":"332b7698-676b-4a3e-8b02-81b1195c5af6","title":"Sr. AI Engineer - FDE (Forward Deployed Engineer) - U.S. Federal Sector","slug":"sr-ai-engineer-fde-forward-deployed-engineer-us-federal-sector-2b0534c9","description":"PLEASE NOTE : Due to federal contract requirements and client site access obligations, U.S. citizenship and eligibility for a U.S. government secret clearance are required to access classified information. The position is based in the Washington, D.C., Maryland, or Virginia metropolitan area and includes periodic on‑site work and client collaboration. Candidates with an active Secret or higher clearance are strongly encouraged to apply. \n The AI Forward Deployed Engineering (AI FDE) team is a highly specialized customer-facing AI team at Databricks. We deliver professional services engagements to help our customers build and productionize first-of-its-kind AI applications. We work cross-functionally to shape long-term strategic priorities and initiatives alongside engineering, product, and developer relations, as well as support internal subject matter expert (SME) teams. We view our team as an ensemble: we look for individuals with strong, unique specializations to improve the overall strength of the team. This team is the right fit for you if you love working with customers, teammates, and fueling your curiosity for the latest trends in GenAI, LLMOps, and ML more broadly. This role can be remote.\n The impact you will have: \n \n Develop cutting-edge GenAI solutions, incorporating the latest techniques from Databricks AI research to solve customer problems\n Own production rollouts of consumer and internally facing GenAI applications\n Serve as a trusted technical advisor to customers across a variety of domains\n Present at conferences such as Data + AI Summit , recognized as a thought leader internally and externally\n Collaborate cross-functionally with the product and engineering teams to influence priorities and shape the product roadmap \n \n What we look for: \n \n Experience building GenAI applications, including RAG, multi-agent systems, Text2SQL, fine-tuning, etc., with tools such as HuggingFace, LangChain, and DSPy\n Expertise in deploying production-grade GenAI applications, including evaluation and optimizations \n Extensive years of hands-on industry data science experience, leveraging common machine learning and data science tools, i.e. pandas, scikit-learn, PyTorch, etc.\n Experience building production-grade machine learning deployments on AWS, Azure, or GCP\n Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience\n Experience communicating and/or teaching technical concepts to non-technical and technical audiences alike\n Passion for collaboration, life-long learning, and driving business value through AI\n [Preferred] Experience using the Databricks Intelligence Platform and Apache Spark™ to process large-scale distributed datasets\n Willing to travel once every 4-8 weeks to see customers (as needed)\n  \n Pay Range Transparency \n Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles.  Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here . \n  \n Local Pay Range\n $182,000 — $250,208 USD \n About Databricks \n Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT\u0026T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn , X , YouTube , and Instagram . Benefits At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here . \n Our Commitment to Diversity and Inclusion \n At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affili","salary_min":182000,"salary_max":250208,"location":"Washington, DC","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["pytorch","data-pipeline","fine-tuning","llm","generative-ai","agents"],"apply_url":"https://databricks.com/company/careers/open-positions/job?gh_jid=8760168002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-28T14:56:34Z","expires_at":"2026-09-29T13:32:32.139503Z","created_at":"2026-08-29T13:32:27.824973Z","updated_at":"2026-08-30T13:32:32.280262Z","company_name":"Databricks","company_slug":"databricks","company_logo_url":"https://www.google.com/s2/favicons?domain=databricks.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/57b56d5b-e1f9-4118-afb8-2bd1f37d7f46"},{"id":"11b73365-9170-4b94-a834-6cf9ce41c2db","company_id":"332b7698-676b-4a3e-8b02-81b1195c5af6","title":"Sr. AI Engineer - FDE (Forward Deployed Engineer) - U.S. Federal Sector","slug":"sr-ai-engineer-fde-forward-deployed-engineer-us-federal-sector-3fa2b5eb","description":"PLEASE NOTE : Due to federal contract requirements and client site access obligations, U.S. citizenship and eligibility for a U.S. government secret clearance are required to access classified information. The position is based in the Washington, D.C., Maryland, or Virginia metropolitan area and includes periodic on‑site work and client collaboration. Candidates with an active Secret or higher clearance are strongly encouraged to apply. \n The AI Forward Deployed Engineering (AI FDE) team is a highly specialized customer-facing AI team at Databricks. We deliver professional services engagements to help our customers build and productionize first-of-its-kind AI applications. We work cross-functionally to shape long-term strategic priorities and initiatives alongside engineering, product, and developer relations, as well as support internal subject matter expert (SME) teams. We view our team as an ensemble: we look for individuals with strong, unique specializations to improve the overall strength of the team. This team is the right fit for you if you love working with customers, teammates, and fueling your curiosity for the latest trends in GenAI, LLMOps, and ML more broadly. This role can be remote.\n The impact you will have: \n \n Develop cutting-edge GenAI solutions, incorporating the latest techniques from Databricks AI research to solve customer problems\n Own production rollouts of consumer and internally facing GenAI applications\n Serve as a trusted technical advisor to customers across a variety of domains\n Present at conferences such as Data + AI Summit , recognized as a thought leader internally and externally\n Collaborate cross-functionally with the product and engineering teams to influence priorities and shape the product roadmap \n \n What we look for: \n \n Experience building GenAI applications, including RAG, multi-agent systems, Text2SQL, fine-tuning, etc., with tools such as HuggingFace, LangChain, and DSPy\n Expertise in deploying production-grade GenAI applications, including evaluation and optimizations \n Extensive years of hands-on industry data science experience, leveraging common machine learning and data science tools, i.e. pandas, scikit-learn, PyTorch, etc.\n Experience building production-grade machine learning deployments on AWS, Azure, or GCP\n Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience\n Experience communicating and/or teaching technical concepts to non-technical and technical audiences alike\n Passion for collaboration, life-long learning, and driving business value through AI\n [Preferred] Experience using the Databricks Intelligence Platform and Apache Spark™ to process large-scale distributed datasets\n Willing to travel once every 4-8 weeks to see customers (as needed)\n  \n Pay Range Transparency \n Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles.  Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here . \n  \n Local Pay Range\n $182,000 — $250,208 USD \n About Databricks \n Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT\u0026T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn , X , YouTube , and Instagram . Benefits At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here . \n Our Commitment to Diversity and Inclusion \n At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affili","salary_min":182000,"salary_max":250208,"location":"Virginia","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["pytorch","fine-tuning","data-pipeline","agents","llm","generative-ai"],"apply_url":"https://databricks.com/company/careers/open-positions/job?gh_jid=8760167002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-28T14:56:31Z","expires_at":"2026-09-29T13:32:32.232912Z","created_at":"2026-08-29T13:32:27.639904Z","updated_at":"2026-08-30T13:32:32.374987Z","company_name":"Databricks","company_slug":"databricks","company_logo_url":"https://www.google.com/s2/favicons?domain=databricks.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/11b73365-9170-4b94-a834-6cf9ce41c2db"},{"id":"bd52fe7c-5d97-4e82-bec6-4431216e869e","company_id":"053355fc-0162-4bb9-b414-cbf7679ee9c8","title":"Senior/Staff FDE - Synthetic Data Generation","slug":"seniorstaff-fde-synthetic-data-generation-9477fabd","description":"About Snorkel \n At Snorkel, we believe meaningful AI doesn’t start with the model, it starts with the data.\n We’re on a mission to help enterprises transform expert knowledge into specialized AI at scale. The AI landscape has gone through incredible changes since 2015, when Snorkel started as a research project in the Stanford AI Lab, to the generative AI breakthroughs of today. But one thing has remained constant: the data you use to build AI is the key to achieving differentiation, high performance, and production-ready systems. We work with some of the world’s largest organizations to empower scientists, engineers, financial experts, product creators, journalists, and more to build custom AI with their data faster than ever before. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler!\n \n About the Role\n Snorkel AI is hiring a Forward Deployed Engineer focused on Synthetic Data Generation to partner with leading AI labs and enterprises on their most critical AI initiatives.\n In this role, you will lead the technical execution of complex customer engagements where synthetic data is used to improve model training, evaluation, and performance. You will translate ambiguous model and data challenges into effective data strategies, build scalable generation and evaluation pipelines, and use experimentation to continuously improve data quality and downstream model outcomes.\n You will work across the full delivery lifecycle—from technical discovery and solution design through implementation, evaluation, and production delivery. You will also identify patterns across engagements and turn successful approaches into reusable capabilities, technical standards, and product improvements.\n Main Responsibilities\n Synthetic Data Generation \u0026 Evaluation\n \n Design and build scalable synthetic data generation, transformation, filtering, and evaluation pipelines for complex AI use cases\n Translate model objectives, failure modes, and data gaps into synthetic data strategies, experiments, and technical specifications\n Develop LLM- and ML-assisted workflows to generate high-quality training and evaluation datasets across targeted behaviors, domains, and edge cases\n Build automated evaluators, quality checks, and measurement frameworks to assess correctness, relevance, diversity, coverage, and adherence to customer requirements\n Design and run experiments to measure the impact of synthetic data on downstream model performance and iteratively improve generation approaches\n Package and deliver production-grade datasets with standardized formats, quality assurance, and clear documentation\n \n Forward Deployed Engineering \u0026 Customer Partnership\n \n Lead technical workstreams from initial solution design through production delivery, navigating ambiguity and making sound technical decisions\n Build, refine, and iterate on solutions that address customer needs, incorporating feedback to ensure the delivered work provides tangible value\n Rapidly prototype and productionize solutions across models, data pipelines, APIs, and custom applications\n Communicate technical tradeoffs, experimental results, and recommendations clearly to technical and cross-functional stakeholders\n Serve as a trusted technical partner to customers and internal delivery teams, resolving complex blockers and driving alignment\n \n Technical Leadership \u0026 Scale\n \n Identify recurring patterns across customer engagements and turn successful solutions into reusable pipelines, evaluators, tooling, and best practices\n Define and improve technical standards for synthetic data generation, experimentation, evaluation, and delivery\n Partner with DaaS Engineering and Product teams to influence platform and product capabilities based on real-world customer needs\n Lead technical design reviews, share expertise, and provide guidance to other engineers\n Stay current with emerging synthetic data, LLM evaluation, and data curation techniques and assess their applicability to customer problems\n \n What We're Looking For\n \n 5+ years of experience in machine learning engineering, data science, applied AI, forward deployed engineering, or a similar technical role\n Strong Python skills and experience building reliable production data or ML systems, including containerizing with Docker and deploying on cloud platforms (e.g., AWS, GCP, or Azure)\n Hands-on experience with LLMs—building model-based applications and data workflows with the modern GenAI/LLM stack, and integrating systems, models, and data sources through APIs\n Strong understanding of ML experimentation and evaluation, including defining metrics and using empirical results to guide technical decisions\n Experience building synthetic data, data augmentation, or model-generated training and evaluation datasets\n Experience with LLM evaluation techniques, including LLM-as-a-judge, model-based evaluation, rubric-based evaluation, or custom evaluators\n Demonstrated ability to take ambiguous technical probl","salary_min":180000,"salary_max":320000,"location":"New York, NY","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["data-pipeline","llm","reinforcement-learning","agents","generative-ai","fine-tuning"],"apply_url":"https://job-boards.greenhouse.io/snorkelai/jobs/6167063004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-27T16:56:44Z","expires_at":"2026-09-29T13:34:01.344588Z","created_at":"2026-08-29T13:34:10.967098Z","updated_at":"2026-08-30T13:34:01.484225Z","company_name":"Snorkel AI","company_slug":"snorkel-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=snorkel.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/bd52fe7c-5d97-4e82-bec6-4431216e869e"},{"id":"99ca5606-9232-4b96-8b12-b49baec86bf5","company_id":"053355fc-0162-4bb9-b414-cbf7679ee9c8","title":"Senior/Staff FDE - CUA","slug":"seniorstaff-fde-cua-6da3e68e","description":"About Snorkel \n At Snorkel, we believe meaningful AI doesn’t start with the model, it starts with the data.\n We’re on a mission to help enterprises transform expert knowledge into specialized AI at scale. The AI landscape has gone through incredible changes since 2015, when Snorkel started as a research project in the Stanford AI Lab, to the generative AI breakthroughs of today. But one thing has remained constant: the data you use to build AI is the key to achieving differentiation, high performance, and production-ready systems. We work with some of the world’s largest organizations to empower scientists, engineers, financial experts, product creators, journalists, and more to build custom AI with their data faster than ever before. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler!\n About the Role\n Snorkel AI is hiring a Forward Deployed Engineer focused on Computer Use Agents to partner with leading AI labs and enterprises on their most critical agentic-AI initiatives.\n In this role, you will lead the technical execution of complex customer engagements involving agents that operate computers, browsers, and software environments to complete realistic, multi-step tasks. You will translate ambiguous product and model challenges into robust task environments, datasets, evaluators, and delivery plans that improve agent reliability and downstream performance.\n You will work across the full delivery lifecycle—from technical discovery and solution design through implementation, evaluation, and production delivery. You will also identify patterns across engagements and turn successful approaches into reusable capabilities, technical standards, and product improvements.\n Main Responsibilities\n Computer Use Agents, Data, and Evaluation\n \n Design and build task environments, datasets, and evaluation workflows for computer-using agents operating across browsers, desktop applications, terminals, and other software interfaces\n Translate customer goals, agent failure modes, and real-world workflows into representative, multi-step tasks with clear success criteria\n Develop data-generation, validation, and quality-assurance pipelines for multimodal and agentic training and evaluation data\n Build automated evaluators, checks, and measurement frameworks to assess task completion, correctness, robustness, efficiency, and adherence to requirements\n Diagnose agent failures across planning, tool use, perception, state management, and interaction with user interfaces; turn findings into improved tasks, data, and evaluations\n Design and run experiments to measure how data, task design, and evaluation changes affect downstream agent performance\n Deliver reusable, production-grade task suites, datasets, and evaluation assets that help customers train, benchmark, and improve computer-use agents\n \n Forward Deployed Engineering \u0026 Customer Partnership\n \n Lead technical workstreams from initial solution design through production delivery, navigating ambiguity and making sound technical decisions\n Build, refine, and iterate on solutions that address customer needs, incorporating feedback to ensure the delivered work provides tangible value\n Rapidly prototype and productionize solutions across models, agent frameworks, APIs, browser or desktop environments, and custom applications\n Communicate technical tradeoffs, experimental results, and recommendations clearly to technical and cross-functional stakeholders\n Serve as a trusted technical partner to customers and internal delivery teams, resolving complex blockers and driving alignment\n \n Technical Leadership \u0026 Scale\n \n Identify recurring patterns across customer engagements and turn successful solutions into reusable task frameworks, evaluators, tooling, and best practices\n Define and improve technical standards for agent task design, environment reliability, evaluation, and delivery\n Partner with DaaS Engineering, Research, and Product teams to influence platform and product capabilities based on real-world customer needs\n Lead technical design reviews, share expertise, and provide guidance to other engineers\n Stay current with emerging agentic-AI, computer-use, evaluation, and data-curation techniques and assess their applicability to customer problems\n \n What We're Looking For\n \n 5+ years of experience in machine learning engineering, software engineering, applied AI, forward deployed engineering, solutions engineering, or a similar technical role\n Strong Python skills and experience building reliable production software, data, or ML systems\n Hands-on experience building, evaluating, or deploying LLM-based or agentic systems, including computer-use agents (CUA)\n Strong understanding of experimentation and evaluation, including LLM-as-a-judge / model-based evaluation, defining metrics, and using empirical results to guide technical decisions\n Experience designing task environments, datasets, and verifiers for agents, including reward \u0026 verifier desi","salary_min":180000,"salary_max":320000,"location":"New York, NY","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["generative-ai","fine-tuning","reinforcement-learning","data-pipeline","agents","llm"],"apply_url":"https://job-boards.greenhouse.io/snorkelai/jobs/6167049004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-27T16:38:34Z","expires_at":"2026-09-29T13:34:01.251345Z","created_at":"2026-08-29T13:34:10.876038Z","updated_at":"2026-08-30T13:34:01.388379Z","company_name":"Snorkel AI","company_slug":"snorkel-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=snorkel.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/99ca5606-9232-4b96-8b12-b49baec86bf5"},{"id":"e82c02c8-1f82-44e8-a6a9-52c07aaa1af9","company_id":"714f360f-a244-487d-b3f0-0c43518a9e66","title":"Machine Learning Engineer II, Responsible AI","slug":"machine-learning-engineer-ii-responsible-ai-748de33f","description":"About Pinterest: \n Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.\n Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the  flexibility to do your best work. Creating a career you love? It’s Possible.\n At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.\n Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here .\n The Responsible AI team is part of the Advanced Technologies Group (ATG), Pinterest’s advanced machine learning team. ATG’s goal is to keep Pinterest at the forefront of machine learning technology across multiple use cases including recommendations, ranking, content understanding, and more. It is an applied team that works horizontally across the company on state of the art AI and ML and works on directly bringing that technology to the product in collaboration with product engineering teams. The team also publishes its work in applied research conferences, but the main goal of the team is to have a direct impact on business metrics.\n At Pinterest our goal is to inspire pinners (our users) to live the life they love. The product is powered by state of the art ML algorithms which are used to understand both the billions of visually rich items on the platform and the interests of our 535M+ monthly active users and recommend inspiring personalized content to them. What this means is that ML at Pinterest is not only multi-modal utilizing image, text, and graph signals as input, but it needs to operate at a very large scale and often in a real time interactive user experience.\n Pinterest is known for being a positive and inspirational place on the internet and we believe that inspiration begins with representation and belonging. The Responsible AI team is a horizontal team that collaborates across the company on various initiatives. These range from Generative AI alignment, evaluation, and mitigations - ensuring these models are safe, bias-free, and aligned with Pinterest’s vision and policies - to championing ML Fairness and developing user-facing features that enhance our product for all users. This includes launching groundbreaking features such as user-controllable skin tone , hair pattern search refinements, and more recently, body type .\n You’ll help shape forward-thinking projects, develop advanced ML approaches rooted in fairness and equitability, and pioneer responsible AI safeguards for emerging technologies. By joining this team, you’ll make a lasting impact on our Pinners, our business, and the evolution of ethical AI at Pinterest.\n What you'll do: \n \n execute on projects in the responsible AI frontier, to identify, avoid, and mitigate bias across a wide range of ML applications at Pinterest including generative AI. \n Collaborate with other engineering teams (trust and safety, user modeling, content understanding,) to leverage their platforms and signals and work with them to collaborate on the adoption and evaluation of Responsible AI practices and ML Fairness tooling across Pinterest.\n Mentor junior engineers on the Responsible AI team and across the company on the R-AI space.\n Work with the team and senior leaders at the company to define and drive technical strategy in this area.\n \n What we're looking for: \n \n Extensive, real-world experience applying advanced ML methods to production systems, with a strong track record in responsible technology - spanning fairness, ethics, and broader societal considerations.\n Deep familiarity with cutting-edge ML architectures (e.g., transformer-based models, 2-tower architectures, LLMs) and their applications in large-scale Search and Recommender Systems.\n Proven ability to measure, deploy, and refine fairness interventions and broader Responsible AI solutions at scale, bridging state-of-the-art research with tangible product impact.\n 2+ years working experience in the engineering teams that build large-scale ML-driven user-facing products\n Masters or PhD in Comp Sci or related fields\n \n Nice to have: \n \n Publications at top ML conferences\n Experience using Cursor, Copilot, Codex, or similar AI coding assistants for development, debugging, testing, and refactoring\n Familiarity with LLM-powered productivity tools for","salary_min":138905,"salary_max":285982,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["generative-ai","code-generation","fine-tuning","llm","machine-learning"],"apply_url":"https://www.pinterestcareers.com/jobs/?gh_jid=8162046","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-27T16:29:11Z","expires_at":"2026-09-29T13:38:53.169272Z","created_at":"2026-08-29T13:39:28.752933Z","updated_at":"2026-08-30T13:38:53.311845Z","company_name":"Pinterest","company_slug":"pinterest","company_logo_url":"https://www.google.com/s2/favicons?domain=www.pinterest.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e82c02c8-1f82-44e8-a6a9-52c07aaa1af9"},{"id":"f42e3d93-afff-4fd2-9053-59de318ca30d","company_id":"76c63eb7-c307-4322-8c2b-c20216feec49","title":"Senior Software Engineer, Agentic Systems","slug":"senior-software-engineer-agentic-systems-0e617d76","description":"Get to Know Us\n\nHorizon3 http://Horizon3.ai is a fast-growing, remote cybersecurity company dedicated to the mission of enabling organizations to proactively find, fix, and verify exploitable attack vectors before criminals exploit them. Our flagship product, the NodeZero™ platform, delivers production-safe autonomous pentests and other key assessment operations that scale across the largest internal, external, cloud, and hybrid cloud environments. NodeZero has been adopted by organizations of all sizes, from small educational institutions to government agencies and Global 100 enterprises. It is used by ITOps/SecOps teams, consulting pentesters, and MSSPs and MSPs.\n\nWe are a fusion of former U.S. Special Operations cyber operators, startup engineers, and formerly frustrated cybersecurity practitioners. We're committed to helping solve our common security problems: ineffective security tools, false positives resulting in alert fatigue, blind spots, \"checkbox\" security culture, the cybersecurity skills shortage, and the long lead time and expense of hiring outside consultants. Collectively, we are a team of learn-it-alls, committed to a culture of respect, collaboration, ownership, and results.\n\n\n\nSummary\n\nWe're building an autonomous, black-box web application penetration tester. It crawls and attacks real production websites the way a skilled human pentester would, finding broken access control, injection, XSS, SSRF, SSTI, and more, under a strict production-safe, no-false-positives mandate.\n\nWe have deep offensive expertise on this team: people who know exactly how to find and exploit these vulnerabilities by hand. What we need is an engineer who can turn that expertise into autonomous agent capability, the reasoning, orchestration, tooling, and evaluation that lets an LLM-driven agent do this work reliably, at scale, and unattended. You'd own and evolve the attack-agent layer: the part of the system that decides what to probe, forms and tests hypotheses, exploits, and verifies, without false positives and without touching anything it shouldn't.\n\nThis is a build role, not a research role. We use models surgically, deterministic-first, LLM-as-scalpel, and the hard problems are in engineering reliability, not chasing benchmarks.\n\n\n\nEssential Functions\n\n - Build and evolve the agent harness and orchestration that turns an LLM into a reliable autonomous pentester, the loop that reasons over an application, forms attack hypotheses, acts, and verifies results.\n\n - Design the tools and tool-shaped feedback the agent uses to probe and exploit, and the structured-output and validation layers that keep it reliable (e.g., hook-enforced mandatory validation, schema-constrained outputs).\n\n - Translate the team's offensive expertise into repeatable agent capabilities — partnering directly with our attackers to encode how they think into something the agent can do consistently.\n\n - Own and grow our evaluation infrastructure: benchmark suites, a failure-mode taxonomy across the pipeline (discovery → hypothesis → exploitation → verification), and regression detection, so we actually know whether the agent is getting better.\n\n - Manage LLM inference in production: model selection, prompt and context engineering, and keeping cost and latency under control (we run on AWS Bedrock with centralized cost tracking).\n\n - Hold the line on production-safety and no-false-positives, every finding the agent reports has to be real and reproducible.\n\n \n\nCompetencies/Requirements\n\n - 5+ years building production software, with strong Python.\n\n - Hands-on experience building LLM-powered applications or agents, tool use / function calling, structured outputs, multi-step orchestration, and the glue that makes it all hold together.\n\n - A track record of making LLMs reliable in production, you've wrestled nondeterminism, designed around model limitations, and shipped something that worked when it mattered.\n\n - Real experience with evaluation: you've built or owned the harness that tells you whether a model or agent change is an improvement, not just a vibe.\n\n - Strong instincts for prompt and context engineering, and the judgment to keep the model's job small and well-scoped.\n\n - Solid software fundamentals — testing, observability, and the discipline to keep a complex agent debuggable.\n\n - Ownership mentality, comfortable owning a critical, fast-moving subsystem end to end.\n\n \n\nDesired/Nice to Have\n\n - Working knowledge of web application security, broken access control, IDOR/BOLA, SQLi, XSS, SSRF, SSTI, enough to collaborate fluently with offensive engineers.\n\n - Experience building eval harnesses or benchmarks specifically for agents (synthetic environments, CVE-based test targets, capture-the-flag-style scoring).\n\n - Experience with agent frameworks, and strong opinions about when not to reach for one.\n\n - Familiarity with graph data models (e.g., Neo4j) for representing application state and attack context.\n\n \n\nWhat makes yo","salary_min":169000,"salary_max":208000,"location":"Remote (US)","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["llm","agents","security","fine-tuning","cloud"],"apply_url":"https://jobs.ashbyhq.com/horizon3ai/e36e9f43-c831-43ac-85b3-782b28bef222/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-26T20:45:13.382Z","expires_at":"2026-09-29T13:36:41.233184Z","created_at":"2026-06-28T14:06:08.698561Z","updated_at":"2026-08-30T13:36:41.369818Z","company_name":"Horizon3 AI","company_slug":"horizon3-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=horizon3.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f42e3d93-afff-4fd2-9053-59de318ca30d"},{"id":"896963d3-d6bb-4619-8398-538ac2eb09ca","company_id":"ed18bbda-3537-4b44-9295-c7b575fce0ff","title":"Staff Enterprise Security Engineer, AI Security","slug":"staff-enterprise-security-engineer-ai-security-9656605f","description":"Who we are  \n At Twilio, we’re shaping the future of communications, all from the comfort of our homes. We deliver innovative solutions to  hundreds of thousands of businesses  and empower millions of developers worldwide to craft personalized customer experiences.\n Our dedication to remote-first work , and strong culture of connection and global inclusion means that no matter your location, you’re part of a vibrant team with diverse experiences making a global impact each day. As we continue to revolutionize how the world interacts, we’re acquiring new skills and experiences that make work feel truly rewarding. Your career at Twilio is in your hands. . \n Hiring and how we work \n We use Artificial Intelligence (AI) to help make our hiring process efficient. That said, every hiring decision is made by real Twilions! \n Also, while we are a remote-first company, you may be asked to report in person on an ad-hoc basis for team gatherings, functional off-sites or customer meetings.  . \n See yourself at Twilio \n Join the team as our next Staff Engineer, Enterprise Security \n About the job \n As a Staff Enterprise Security Engineer, you will be a technical leader within the EntSec team, responsible for the foundational security posture of our organization. You will serve as an SME on the technical strategy and engineering execution for securing the AI ecosystem in our Enterprise, moving beyond policy into building the foundational \"decision infrastructure\" and technical guardrails that allow the enterprise to innovate with AI at scale.\n You will also guide strategic direction and collaboration across enterprise security domains. You will need a background in security engineering and the cross-functional influence necessary to solve ambiguous, large-scale problems. Leveraging expertise in security assessments,  threat modeling, identity and access control principles, and data protection, you will architect and build preventative guardrails and mitigate new risks introduced by first and third-party AI agents in our Enterprise.\n Responsibilities \n \n Design and implement secure reference architectures for Enterprise AI platforms that secures every Twilion’s engagement with them, ensuring data integrity, regulatory compliance, and resilience against evolving AI threats.\n Establish a definitive framework for AI vetting, driving the cultural and policy shifts needed to institutionalize this strategic mindset across the organization.\n Collaborate with cross functional partners to develop and set the long term roadmap for agentic AI identity and posture management, ensuring cohesive strategies for reducing risk from agentic AI use.\n Maintain and improve our enterprise security posture through high-quality code (Python, Go, or similar) and infrastructure management via IAC.\n Act as a technical mentor to junior engineers and a strategic advisor to leadership on the evolving AI landscape.\n \n Qualifications: \n Not all applicants will have skills that match a job description exactly. Twilio values diverse experiences in other industries, and we encourage everyone who meets the required qualifications to apply. \n Required: \n \n 7+ years of experience in security engineering or infrastructure security\n 2+ years of experience leading teams in a technical capacity/staff engineering capacity at an enterprise.\n Expertise in cloud security (AWS, GCP) and container security (Kubernetes).\n Proven track record of designing and deploying complex security systems at scale.\n Strong proficiency in programming languages such as Python, Go, or Java.\n \n Desired: \n \n Experience in building, deploying and reviewing automation for complex security workflows, including use of both AI-driven and traditional automation tools.\n Excellent communication skills with the ability to explain complex AI security risks to non-technical stakeholders.\n \n Location \n This role will be remote, but is not eligible to be hired in San Francisco, CA, Oakland, CA, San Jose, CA, or the surrounding areas.\n Travel  \n We prioritize connection and opportunities to build relationships with our customers and each other. For this role, you may be required to travel occasionally to participate in project or team in-person meetings.\n Compensation \n *Please note the salary range information provided applies only to candidates residing in California, Colorado, Hawaii, Illinois, Maryland, Massachusetts, Minnesota, New Jersey, New York, Vermont, Washington D.C., and Washington State due to local requirements. Compensation for candidates in other locations will be discussed during the hiring process. Please note that hiring for this role is not restricted to the locations listed above. \n . The estimated pay ranges for this role are as follows:\n \n Based in Colorado, Hawaii, Illinois, Maryland, Massachusetts, Minnesota, Vermont or Washington D.C. : $155,520 - $194,400.\n Based in New York, New Jersey, Washington State, or California (outside of the San Francisco Bay area)","salary_min":182960,"salary_max":228700,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"lead","tags":["agents","payments","security","fine-tuning"],"apply_url":"https://job-boards.greenhouse.io/twilio/jobs/8160279","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-26T19:25:54Z","expires_at":"2026-09-29T13:39:44.825255Z","created_at":"2026-08-29T13:40:25.23133Z","updated_at":"2026-08-30T13:39:44.96052Z","company_name":"Twilio","company_slug":"twilio","company_logo_url":"https://www.google.com/s2/favicons?domain=twilio.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/896963d3-d6bb-4619-8398-538ac2eb09ca"},{"id":"a60887bd-18b6-4819-b8ac-a8ce688f7d3f","company_id":"a0000000-0000-0000-0000-000000000003","title":"Machine Learning Research Scientist, Evaluations","slug":"machine-learning-research-scientist-evaluations-47ca5c35","description":"Scale works with the industry's leading AI labs to provide high quality data and accelerate progress in GenAI research. We are looking for Research Scientists and Research Engineers with expertise in LLM post-training (SFT, RLHF, reward modeling) and evaluation. This role is on the evaluation pod within the GenAI Research Organization and will focus on building benchmarks and diagnosing model failure modes in both text and multimodal modalities.\n In this role, you will develop rigorous evaluations and diagnostic methods that reveal where frontier models fail and why. You will collaborate with researchers and engineers to define best practices in evaluation-driven AI development. You will also partner with top foundation model labs to translate failure analysis into technical and strategic input on the next generation of generative AI models.\n You will: \n \n Analyze model behavior to identify, characterize, and diagnose failure modes in frontier LLMs and Agents.  You’ll identify everything from capability gaps and reasoning errors to robustness and alignment issues, all focusing on RCA.\n Design and build benchmarks and evaluation methods that measure LLM capabilities in both text and multimodal modalities.\n Apply post-training expertise (SFT, RLHF, reward modeling) to connect observed failures to the data and training interventions that address them.\n Publish research findings in top-tier AI conferences.\n \n Ideally you’d have: \n \n Ph.D. or Master's degree in Computer Science, Machine Learning, AI, or a related field.\n Deep understanding of deep learning, reinforcement learning, and large-scale model fine-tuning.\n Experience with post-training techniques such as RLHF, preference modeling, or instruction tuning, and with LLM evaluation or benchmark development.\n Excellent written and verbal communication skills.\n Published research in areas of machine learning at major conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, etc.) and/or journals.\n Previous experience in a customer facing role.\n Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend. \n Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is:\n $180,600 — $225,750 USD \n PLEASE NOTE:  Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants. \n About Us: \n At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Ernst \u0026 Young, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications. \n We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status.  \n We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at accommodations@scale.com. Please see the United States Department of Labor's Know Your Rights poster for additional information. \n We comply with the United States Department of Labor's Pay Transparency provision .  \n PLEASE NOTE: We co","salary_min":180600,"salary_max":225750,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["deep-learning","fine-tuning","generative-ai","reinforcement-learning","search","nlp","llm","evaluation"],"apply_url":"https://job-boards.greenhouse.io/scaleai/jobs/4728014005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-26T18:51:42Z","expires_at":"2026-09-29T13:31:40.355249Z","created_at":"2026-08-27T13:31:38.7307Z","updated_at":"2026-08-30T13:31:40.501539Z","company_name":"Scale AI","company_slug":"scale-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=scale.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a60887bd-18b6-4819-b8ac-a8ce688f7d3f"},{"id":"44046e1e-69ad-4692-9110-7bb87edaadaf","company_id":"43dd17db-bec3-4ebb-a432-f71d57a9aa47","title":"Staff Software Engineer, Imaging","slug":"staff-software-engineer-imaging-4ba96095","description":"THE OPPORTUNITY\n\ninsitro is a physical AI company dedicated to unlocking causal human biology and accelerating the delivery of better medicines to patients. Our unique Virtual Human™ platform identifies novel, high-impact genetic intervention points, which our TherML™ platform translates into therapeutics—whether small molecules, biologics, or oligos. With multiple programs in metabolic disease and neuroscience advancing toward the clinic, and our first IND submission slated for the second half of this year, we are at a pivotal inflection point.\n\nAs a Staff Software Engineer on our Imaging Software team, you will define and expand our computer vision and ML infrastructure across the full imaging data lifecycle — from on-microscope acquisition to high-throughput ML pipelines. You'll build the platform features that make novel imaging modalities and ML-derived phenotypes integral to our discovery workflows, partnering daily with lab scientists, ML scientists, and our microscopy team to turn research prototypes into validated screening workflows that run reliably at laboratory automation scale. This is a chance to set the technical direction for how imaging, automation, and machine learning converge in drug discovery.\n\nBased in South San Francisco, this position reports directly to the Director of Imaging, Cellular Machine Learning and offers an in-person hybrid schedule of three days per week.\n\n\n\n\nRESPONSIBILITIES\n\nPlatform \u0026 Tooling\n\n• Platform Enablement: Partner with lab and ML scientists to design, develop, and scale the platform capabilities needed to run and interpret ML-powered high-content imaging screens\n\n• User Tooling: Build and evolve robust tools and interactive interfaces for data exploration, quality assessment, and visualization so scientists can iterate quickly on experimental data\n\n• Architectural Ownership: Own complex, end-to-end projects, making thoughtful architectural trade-offs, and delivering incrementally with long-term maintainability in mind\n\nProduction Hardening \u0026 Data Integrity\n\n• Production Hardening: Scale and harden complex image processing and ML workflows, taking them from research prototypes to systems that reliably process millions of images per day\n\n• Data Integrity: Set and uphold best-in-class practices for data integrity, lineage tracking, reproducibility, and observability across the entire imaging data lifecycle\n\n• Documentation: Write clear, exemplary technical specifications and documentation that others build on\n\nCross-Functional Partnership\n\n• Scientific Translation: Work closely with lab scientists, ML scientists, and microscopy teams to translate complex experimental needs into clear, actionable technical plans and shipped software\n\n• Mentorship: Raise the technical bar across the team by sharing knowledge and mentoring other engineers\n\n \n\n\nABOUT YOU\n\nExperience \u0026 Qualifications\n\n• Proven Tenure: 8+ years of professional experience building and operating production-grade software and high-throughput data pipelines, primarily in Python\n\n• ML Platform Depth: You have designed, built, and deployed scientific computing pipelines, visualizations, and QC processes for large-scale imaging or similarly high-dimensional datasets\n\n• Distributed Systems Stack: Hands-on experience with a Python-first ML stack, distributed compute (e.g., PyTorch/Lightning, Ray, Kubernetes), and workflow orchestration (e.g., Argo, Airflow, or redun)\n\n• End-to-End Delivery: A track record of owning complex systems from architecture through production operation\n\nCore Competencies\n\n• Cross-Functional Partnership: You thrive alongside scientists and excel at translating abstract research needs into practical, scalable software\n\n• Mission-Driven: You're motivated by enabling scientific breakthroughs through robust platform engineering\n\n• Force Multiplier: You enjoy mentoring and leveling up the engineers around you \n\n\n\n\nCOMPENSATION \u0026 BENEFITS AT INSITRO\n\nOur target starting salary for successful US-based applicants for this role is $219,000 - $233,000. To determine starting pay, we consider multiple job-related factors including a candidate's skills, education and experience, market demand, business needs, and internal parity. We may also adjust this range in the future based on market data.\n\nThis role is eligible for participation in our Annual Performance Bonus Plan (based on company targets by role level and annual company performance) and our Equity Incentive Plan, subject to the terms of those plans and associated policies.\n\n\n\nIn addition, insitro also provides our employees:\n\n - 401(k) plan with employer matching for contributions\n\n - Excellent medical, dental, and vision coverage as well as mental health and well-being support\n\n - Open, flexible vacation policy\n\n - Paid parental leave of at least 16 weeks to support parents who give birth, and 10 weeks for a new parent (inclusive of birth, adoption, fostering, etc)\n\n - Quarterly budget for books and","salary_min":219000,"salary_max":233000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["healthcare","payments","data-pipeline","fine-tuning","pytorch","computer-vision","distributed-systems"],"apply_url":"https://jobs.ashbyhq.com/insitro/ff6605ae-4961-4a06-b656-6d7dc665d990/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-25T22:43:18.642Z","expires_at":"2026-09-29T13:36:50.099247Z","created_at":"2026-08-26T13:36:56.743297Z","updated_at":"2026-08-30T13:36:50.234419Z","company_name":"Insitro","company_slug":"insitro","company_logo_url":"https://www.google.com/s2/favicons?domain=insitro.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/44046e1e-69ad-4692-9110-7bb87edaadaf"},{"id":"d11674ab-ef1f-4e18-b47e-95433c82fadf","company_id":"286f7739-be56-491a-bcc7-de1df064fcd4","title":"Principal Product Manager, AI Agents \u0026 MCP","slug":"principal-product-manager-ai-agents-mcp-2e8505a7","description":"Amplitude is the leading AI analytics platform, helping over 4,700 customers—including Atlassian, Burger King, NBCUniversal, and Square—build better products and digital experiences. With powerful AI Agents embedded across our platform, teams can analyze, test, and optimize user experiences faster than ever. Ranked #1 across multiple categories in G2’s Winter 2026 Report, Amplitude is the best-in-class solution for product, data, and marketing teams. Learn more at amplitude.com .\n As an organization, we deliver for our customers by living our values. We operate from a place of humility, take ownership of problems and successes, approach challenges with a growth mindset, and put our customers at the center of everything we do.\n Amplitude’s Commitment to Diversity Equity \u0026 Inclusion (DEI): Amplitude believes that diversity enables the creation of better products, improves the ability to solve complex problems, and drives more powerful solutions. We strive to create an environment of inclusion—one focused on psychological safety, empathy, and human connection—that will allow employees of all backgrounds to thrive.\n About the Role \u0026 Team\n Agentic Amplitude is the team that’s responsible for Amplitude's AI agent strategy. The team shipped the Amplitude Global Agent, Custom agents, and our MCP surface, and continues to deliver new products at a fast pace.\n The team is small, moves fast and thrives on curiosity, autonomy, and strong cross-functional partnership. We operate with a bias toward rapid iteration and refine our approach as the underlying technology and models advance.\n This role is the product lead for this surface area. The successful candidate will set the strategy and vision and lead the design and engineering teams to deliver new innovations, while serving as the company’s thought leader on agentic products.\n Responsibilities\n \n Define the direction for Amplitude's agents and MCP surfaces over the next 12 to 24 months, and determine the priorities that will guide investment.\n Own the product vision for how AI agents help our customers to get to insights faster, make decisions with greater confidence, and take the right actions to improve their products.\n Advance our MCP capabilities so that customers can work with Amplitude from their coding agent, CLI, or IDE, in addition to our own user interface.\n Create the evals, quality standards, and feedback mechanisms required to measure customer value and iterate on the agent experience.\n Talk regularly with customers to understand their workflows and identify new opportunities and use cases for AI agents to deliver value.\n Partner closely with the field organization to enable them to position, demo, and support AI Agent products effectively.\n Serve as a thought leader on agentic products, establishing best practices both internally and with customers.\n \n Measures of Success\n \n A clear and inspiring agent and MCP strategy that leadership and the field organization can articulate consistently.\n Increased adoption of existing agents and MCP capabilities, complemented by new agentic products that address emerging customer needs.\n Broad MCP adoption within customer workflows outside the Amplitude user interface.\n \n Qualifications\n \n 7+ years of product management experience, including taking 0-to-1 products from concept through delivery.\n 3+ years building products on AI/ML technologies, with recent hands-on experience in delivering agentic products\n Fluency in the latest LLM trends, models, and tools, with a strong understanding of context management, latency, eval methods, and common failure modes.\n Experience building systems that measure whether a probabilistic product is getting better over time.\n A demonstrated record of executing complex initiatives in close partnership with engineering and design. \n Comfort operating amid ambiguity, rapid change, and a roadmap that evolves alongside the technology.\n A consistent history of setting ambitious goals and achieving them through clear strategy, disciplined execution, and effective collaboration.\n \n Our values:\n At Amplitude, our values guide how we show up for one another and for our customers:\n \n Humility: We operate from a place of empathy and openness, seeking to understand many points of view.\n Ownership: We take the initiative to solve problems that drive our shared company success.\n Growth Mindset: We’re tenacious in the face of challenges and seek feedback in order to grow ourselves and others.\n Customer Centricity: We put the customer at the center of everything we do and are deeply committed to their success.\n \n We care about the well-being of our team: We offer competitive pay and benefits packages that reflect our commitment to the health and well-being of our Ampliteers.\n Some of our benefit programs include:\n \n Excellent ​M​edical, ​D​ental and ​V​ision insurance coverages, with 100% employer-paid premiums for employee Medical, ​Dental,​ ​​​​​​​","salary_min":237000,"salary_max":356000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["llm","fine-tuning","agents","payments"],"apply_url":"https://job-boards.greenhouse.io/amplitude/jobs/8746352002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-25T16:28:52Z","expires_at":"2026-09-29T13:49:14.796249Z","created_at":"2026-08-25T18:33:39.328082Z","updated_at":"2026-08-30T13:49:14.92287Z","company_name":"Amplitude","company_slug":"amplitude","company_logo_url":"https://www.google.com/s2/favicons?domain=amplitude.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/d11674ab-ef1f-4e18-b47e-95433c82fadf"},{"id":"9a42e317-7954-4330-9e7d-59cad3214bb9","company_id":"8115806a-6d9c-48b9-9a92-5eb180bbd1ef","title":"Applied Research Scientist, AI Research","slug":"applied-research-scientist-ai-research-428d0a90","description":"Descript's Research team builds the models behind the product's most distinctive features: Video Regenerate and lipsync, video translation, zero-shot voice and roomtone cloning, and Studio Sound. We don't build general-purpose generative models. We pick specific problems in the editing workflow and build specialized models for them. This isn't research for its own sake. Everything we build is meant to ship, and most of it has, going from prototype to a production feature used by millions of creators within months.\n This role is focused on multimodal understanding: training models to perceive edited media the way a human video editor does. Underlord, our AI editing agent, reasons about a project largely through a textual representation of it. Giving it direct perception of the media it's working on is what will let it judge its own output and reason about the creative choices in an edit, not just the structure of a project. It's also an open research problem, since there's no settled way to represent or evaluate editorial craft, whether a cut lands or whether the pacing works. We have a unique dataset to work with.\n Some recent work from the team:\n \n Audio editing by latent inpainting : regenerating a masked span of speech \n Video Regenerate : regenerating a speaker's lower face to match new or translated audio\n Jumpcut Smoothing : generating a bridge across a cut so the join plays like a continuous take\n Anchored Tree Sampling : tree-based imputation that bounds drift in long video generation\n PoDAR : disentangling power from semantics in audio latents to make them easier to model\n \n More at descript.com/research .\n What you'll do\n \n Multimodal understanding: build vision-language systems that let Descript's agentic editing features reason over the visual and audio content of a project.\n Evaluation: design the benchmarks and evals that make editorial quality measurable, and that balance quality against cost and latency.\n Data: build the datasets your work depends on, including synthetic data generation where real examples don't exist at scale.\n Training: train specialized models from scratch or fine-tune existing foundation models, whichever gets the capability we need.\n Shipping: take models from prototype to production with the agent and engineering teams.\n Direction-setting: identify the next research direction that should become a Descript feature, not just a paper. More senior candidates should expect to own this directly; more junior candidates will grow into it.\n Publishing: take your work to academic venues if you'd like. We support it, but it isn't a requirement of the role.\n \n What you bring\n Required\n \n Proven ability to design and implement deep learning algorithms, demonstrated by publications, open-source work, or models you've shipped.\n Strong programming skills and deep fluency in PyTorch.\n A track record of generating new ideas in machine learning. You produce more ideas than you can implement, and once an experiment setup is established, you can run and evaluate many of them quickly rather than being bottlenecked on infrastructure.\n Strong experimental judgment. You test ideas fast, and you're honest with yourself and the team about which ones don't pan out.\n Clear written and verbal communication, including when a direction isn't working, so the team doesn't waste time following a lead that's already dead.\n A PhD or Master's in deep learning or a related field, or equivalent experience. We care about the track record more than the credential.\n \n At least one of the following must be true:\n \n Lead or first author of an accepted publication in a top venue: CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, or similar.\n Played a key role in shipping a production feature with deep learning as a core component.\n \n More senior candidates (Senior and Staff) should also bring a track record of owning research direction rather than executing a plan handed to them, and experience mentoring or technically leading other researchers or engineers.\n Where breadth helps\n Direct experience in multimodal understanding is welcome but not required, and we don't require domain-specific expertise in computer vision or speech and audio. Our team spans both, and strong general deep learning ability transfers. We hire against the bar above, and then expect you to grow into the domain. Depth in any of these is a strong signal:\n \n Vision-language models and multimodal understanding.\n Generative modeling for video, audio, or images.\n Post-training, fine-tuning, and RL on large foundation models.\n Building evaluation systems for generative or agentic outputs where metrics resist clean definitions.\n Taking a research idea through to a shipped, production-facing feature.\n \n Compensation and benefits\n Base salary range: $197,000–$262,500, plus equity and benefits. Final offer amounts will carefully consider multiple factors, including prior experience, expertise, location, and level, and may vary from the amount above.\n  \n IMPO","salary_min":197000,"salary_max":262500,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["generative-ai","deep-learning","fine-tuning","computer-vision","pytorch","healthcare","agents","research"],"apply_url":"https://boards.greenhouse.io/descript/jobs/7967440003?gh_jid=7967440003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-25T00:18:24Z","expires_at":"2026-09-29T13:35:54.489992Z","created_at":"2026-08-25T18:27:44.358668Z","updated_at":"2026-08-30T13:35:54.624927Z","company_name":"Descript","company_slug":"descript","company_logo_url":"https://www.google.com/s2/favicons?domain=descript.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/9a42e317-7954-4330-9e7d-59cad3214bb9"},{"id":"fef86a6f-88bb-4446-95f7-e78d16d7e82d","company_id":"a0000000-0000-0000-0000-000000000001","title":"Applied AI Engineer, Beneficial Deployments (Life Sciences)","slug":"applied-ai-engineer-beneficial-deployments-life-sciences-59ac98a0","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About Beneficial Deployments Beneficial Deployments ensures AI reaches and benefits the communities that need it most. We partner with nonprofits, foundations, and mission-driven organizations to deploy Claude in education, global health, economic mobility, and life sciences — focusing on raising the floor for those who need it most.\n About the Role We're looking for an Applied AI Engineer to join our Beneficial Deployments team, focused on maximizing the impact of Claude in the life sciences. Our goal is ambitious: accelerate scientific progress from R\u0026D through translation by an order of magnitude. That means making Claude the go-to tool for the life sciences ecosystem from early discovery in academia to paradigm shifting biotech to reimaging pharma pipelines  — and building the technical infrastructure to back that up.\n You'll work directly with flagship research partners like The Howard Hughes Medical Institute (HHMI) and The Allen Institute, embedded in their scientific workflows. This isn't consulting from the outside — you'll be building alongside their engineers, prototyping agents that fit into real research pipelines, and developing the ecosystem-level tooling (MCP servers, benchmarks, reusable agent skills) that extends Claude's usefulness across the broader life sciences community. This role will be part of the founding Beneficial Deployments Applied AI team focused on bringing life sciences closer to the frontier.\n Responsibilities \n \n Partner deeply with flagship life sciences research institutions — understand their scientific workflows end-to-end, build hands-on with their engineering teams, and help take projects from early exploration to production systems integrated into how they do science day-to-day.\n Develop reusable ecosystem infrastructure, like MCP servers for domain-specific data sources (genomics platforms, literature databases, experimental repositories), instruments, scientifically-grounded benchmarks, and agent skills that other institutions can adopt without starting from scratch.\n Identify what's actually hard about deploying AI in life sciences (heterogeneous data, auditability requirements, the prototype-to-trust gap) and feed those findings back to product, engineering, and research.\n Create technical content and documentation that lets partners self-serve, so what works for one institution can scale globally without the same level of hand-holding.\n \n You Might Be a Good Fit If You Have: \n \n Deep research experience in life sciences, biomedical research, or scientific computing. Bonus if you've studied genomics, neuroscience, or drug discovery specifically and are comfortable getting deeply technical with academics.\n Experience building LLM-powered tools or applications: prompting, context engineering, agent architectures, evaluation frameworks.\n Builder credibility from shipping production code as a software engineer, forward-deployed engineer, or technical founder.\n A scrappy mentality–comfortable wearing multiple hats, building from scratch, driving clarity in ambiguous situations, and doing whatever it takes to further the mission.\n The annual compensation range for this role is listed below. \n For sales roles, the range provided is the role’s On Target Earnings (\"OTE\") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\n Annual Salary:\n $280,000 — $320,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.\n Visa sponsorship:  We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.\n We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourse","salary_min":280000,"salary_max":320000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["healthcare","fine-tuning","alignment","llm"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5021015008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-24T22:31:29Z","expires_at":"2026-09-29T13:30:13.399752Z","created_at":"2026-08-25T18:26:11.30816Z","updated_at":"2026-08-30T13:30:13.543553Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/fef86a6f-88bb-4446-95f7-e78d16d7e82d"},{"id":"30b225f4-4652-4168-a97b-c7af2308a23d","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Staff Security Engineer, Proactive Security - AI","slug":"staff-security-engineer-proactive-security-ai-de8a9beb","description":"About the Team \n At DoorDash we’re building the industry’s most scalable and reliable delivery network to support our three-sided marketplace of consumers, merchants, and Dashers. Security Engineering is paramount to the success of our business, and DoorDash Security aspires to be one the world’s most admired Security Engineering team. We are committed to building the world's most trusted on-demand, logistics engine for delivery! We're expanding our team of great minds to help us secure and maintain a 24x7, no downtime, global infrastructure system that powers DoorDash’s multi-sided marketplace of consumers, merchants, and drivers.\n About the Role \n Our Proactive Security Engineering team is looking for a Staff Security Engineer, Proactive Security to execute on AI Security Product Engineering following a forward deployed engineering Pod model.\n You will be a part of our inclusive, collaborative forward deployed engineering team responsible for building “paved road” Security Product controls directly into our AI surfaces for our Customer, Merchant and Dasher products to ensure a safe, secure and resilient delivery network. This is not a classic Product Security role performing reviews and operating platform products, this is a forward deployed model where we operate in Pods that focus on domain code bases to build and ship AI Security Products including Hardened Product Agents, AI Guardrails, MCP Gateways and many upcoming AI Hardening initiatives. \n This is a US remote position reporting directly to the Manager of our Proactive Security Engineering team. \n You’re excited about this opportunity because you will… \n \n Co-lead the technical direction and roadmap for secure and responsible building of AI Security Products at DoorDash.\n Partner cross-functionally with Product Engineering, Legal, Security Engineering Platform, Data teams and XFN partners to build “paved road” proactive security controls that enable secure by design AI products into DoorDash eco-system. Examples of AI Security Products this team has built include Hardened Product Agents, AI Guardrails, MCP Gateways and many upcoming AI Hardening initiatives. \n Execute rigorous cross-brand AI enabled vulnerability discovery, multi-level harnesses and automated remediation to protect the company against Mythos-era equipped adversaries. \n Mentor and coach earlier career engineers, setting and enforcing exceptional standards for Operational Excellence and Software Engineering.\n \n We’re excited about you because… \n \n 10+ years of experience as a Security Product Engineer at an Internet scale organization of demonstrated technical leadership building outcomes at global scale.\n Master’s degree in Computer Science, Security Engineering, a related field or equivalent work experience.\n Embed with product teams to design, build, and deploy security controls for consumer, merchant, and Dasher products.\n Develop reusable “paved road” solutions that help teams build secure AI products by default.\n Partner with Product Engineering, Legal, Data, Security Platform, and other teams to translate security and regulatory requirements into practical engineering controls.\n Demonstrated technical leadership in designing scalable, AI enabled and ergonomic Security Review threat modeling.\n Hands-on experience securing LLM or agentic systems, or demonstrated ability to move quickly into the space from adjacent depth in authorization, sandboxing, or untrusted input handling.\n Demonstrated builder in one or more object oriented languages (e.g. Go, Java) designing technical Security Engineering controls and processes to meet business and regulatory requirements. \n Exceptional problem solving of complex, systemic issues that require audacious thinking, rigor and creativity.\n \n \n Exceptional analytical and investigative abilities with hands-on experience leading root cause analysis for security incidents and driving long term roadmaps to remediate.\n \n \n Exceptional verbal and written communication skills - you will confidently shape and lead the Security Product Engineering within your assigned AI Security domain across core engineering and product teams to security preserving control outcomes. \n \n Why You’ll Love Working at DoorDash \n \n We are leaders - Leadership is not limited to our management team. It’s something everyone at DoorDash embraces and embodies.\n We are doers - We believe the only way to predict the future is to build it. Creating solutions that will lead our company and our industry is what we do -- on every project, every day. \n We are learners - We’re not afraid to dig in and uncover the truth, even if it’s scary or inconvenient. Everyone here is continually learning on the job, no matter if we’ve been in a role for one year or one minute.\n We are customer-obsessed - Our mission is to grow and empower local economies. We are committed to our customers, merchants, and dashers and believe in connecting people with p","salary_min":193800,"salary_max":285000,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"lead","tags":["llm","security","agents","cloud","healthcare","fine-tuning"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8154315","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-24T18:30:40Z","expires_at":"2026-09-29T13:49:21.802144Z","created_at":"2026-08-26T13:49:31.761186Z","updated_at":"2026-08-30T13:49:21.930778Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/30b225f4-4652-4168-a97b-c7af2308a23d"},{"id":"e9e8b821-5286-4e5f-b57b-788ca2e99b5d","company_id":"e3915539-5a8f-4461-9f26-06366a918674","title":"Mission Operations Product Manager, Group 5 Mission Autonomy","slug":"mission-operations-product-manager-group-5-mission-autonomy-b3c15eb0","description":"Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years.\n WHAT YOU’LL DO   \n \n Develop Mission Autonomy products that support the operation of Group 5 autonomous air vehicles \n Drive product development for the entire lifecycle from initial requirements development to fielding\n Define product roadmaps and timelines, guide cross-functional product decisions, and manage engineering risk\n Working closely with engineering leads and business development, evaluate trades to enable exploration of early designs\n Develop autonomy behavior and supporting product requirements. Collaborate with engineering teams to implement requirements\n Work collaboratively with cross-functional teams, including software engineers, hardware engineers, test engineers, and program managers, to define test requirements and ensure alignment between system capabilities and mission objectives in complex operational scenarios\n Plan, execute, and analyze mission autonomy tests in simulated environments that replicate real-world scenarios to validate system performance and identify areas for improvement, iterating rapidly to refine autonomy behaviors and supporting products\n Support business development teams by being the product voice at the table with customers through customer briefings/demonstrations/exercises\n Support business development in creating and maintain updated presentation templates of product capabilities that can be used as an 80% solution for customer briefs\n \n   \n REQUIRED QUALIFICATIONS   \n \n Operational experience as a Pilot or CSO/NFO of USAF, USN, USMC 4th or 5 th generation fighter/bomber aircraft or RPAs \n Self-starter with high ownership, mission-oriented mentality. Desire to see the product meet the need of the end customer\n Strong analytical skills, with the ability to interpret test data, diagnose system behaviors, and recommend actionable improvements\n Organized and detail-oriented, with strong verbal \u0026 written communication skills\n Proven track record of working collaboratively with multidisciplinary teams to achieve project milestones and deliverables\n Experience in leading system design and development from initial concept through delivery to customers\n Ability to leverage operational experience and technical knowledge to make educated decisions and leverage engineering trades in the absence of clear customer requirements\n Willingness to work flexible hours. Up to 25% travel as required for customer engagements and product development activities\n Active U.S. Top Secret security clearance and experience with Special Access Programs   \n \n PREFERRED QUALIFICATIONS   \n \n 5th generation aircraft pilot experience preferred \n Weapons school instructor, weapons school graduate, test pilot school graduate, Developmental or Operational Test experience\n Bachelor’s degree in STEM, or equivalent experience in a relevant field\n Direct experience in the development, testing, and deployment of mission autonomy systems in a defense or aerospace context\n Familiarity with agile software development practices \n Knowledge of current industry and military standards related to unmanned aerial systems and their operation within national airspace\n Familiarity with the principles of mission autonomy, unmanned systems, and the integration of multiple assets into cohesive operational frameworks\n US Salary Range\n $166,000 — $220,000 USD \n The salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considerations. Highly competitive equity grants are included in the majority of full time offers; and are considered part of Anduril's total compensation package. Additionally, Anduril offers top-tier benefits for full-time employees, including:   \n  \n Benefits \n At Anduril, we invest in our people. Our comprehensive, competitive benefits package (available at little to no cost to employees) ensures you’re supported in health, recovery, and whatever comes next.  For more information, Explore Our Benefits . \n  \n \n Protecting Yourself from Recruitment Scams \n Anduril is committed to maintaining the integrity of our Talent acquisition process and the security of our candidat","salary_min":166000,"salary_max":220000,"location":"Washington, DC","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["payments","cloud","fine-tuning","computer-vision"],"apply_url":"https://boards.greenhouse.io/andurilindustries/jobs/5217951007?gh_jid=5217951007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-24T16:55:07Z","expires_at":"2026-09-29T13:37:16.484793Z","created_at":"2026-08-25T18:28:18.996633Z","updated_at":"2026-08-30T13:37:16.620536Z","company_name":"Anduril","company_slug":"anduril","company_logo_url":"https://www.google.com/s2/favicons?domain=anduril.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e9e8b821-5286-4e5f-b57b-788ca2e99b5d"},{"id":"d1680506-12cb-44c0-8cdd-d0bee0cdf4b4","company_id":"e3915539-5a8f-4461-9f26-06366a918674","title":"Mission Operations Product Manager, Group 5 Mission Autonomy","slug":"mission-operations-product-manager-group-5-mission-autonomy-9d4ddcf9","description":"Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years.\n WHAT YOU’LL DO   \n \n Develop Mission Autonomy products that support the operation of Group 5 autonomous air vehicles \n Drive product development for the entire lifecycle from initial requirements development to fielding\n Define product roadmaps and timelines, guide cross-functional product decisions, and manage engineering risk\n Working closely with engineering leads and business development, evaluate trades to enable exploration of early designs\n Develop autonomy behavior and supporting product requirements. Collaborate with engineering teams to implement requirements\n Work collaboratively with cross-functional teams, including software engineers, hardware engineers, test engineers, and program managers, to define test requirements and ensure alignment between system capabilities and mission objectives in complex operational scenarios\n Plan, execute, and analyze mission autonomy tests in simulated environments that replicate real-world scenarios to validate system performance and identify areas for improvement, iterating rapidly to refine autonomy behaviors and supporting products\n Support business development teams by being the product voice at the table with customers through customer briefings/demonstrations/exercises\n Support business development in creating and maintain updated presentation templates of product capabilities that can be used as an 80% solution for customer briefs\n \n   \n REQUIRED QUALIFICATIONS   \n \n Operational experience as a Pilot or CSO/NFO of USAF, USN, USMC 4th or 5 th generation fighter/bomber aircraft or RPAs \n Self-starter with high ownership, mission-oriented mentality. Desire to see the product meet the need of the end customer\n Strong analytical skills, with the ability to interpret test data, diagnose system behaviors, and recommend actionable improvements\n Organized and detail-oriented, with strong verbal \u0026 written communication skills\n Proven track record of working collaboratively with multidisciplinary teams to achieve project milestones and deliverables\n Experience in leading system design and development from initial concept through delivery to customers\n Ability to leverage operational experience and technical knowledge to make educated decisions and leverage engineering trades in the absence of clear customer requirements\n Willingness to work flexible hours. Up to 25% travel as required for customer engagements and product development activities\n Active U.S. Top Secret security clearance and experience with Special Access Programs   \n \n PREFERRED QUALIFICATIONS   \n \n 5th generation aircraft pilot experience preferred \n Weapons school instructor, weapons school graduate, test pilot school graduate, Developmental or Operational Test experience\n Bachelor’s degree in STEM, or equivalent experience in a relevant field \n Direct experience in the development, testing, and deployment of mission autonomy systems in a defense or aerospace context\n Familiarity with agile software development practices  \n Knowledge of current industry and military standards related to unmanned aerial systems and their operation within national airspace\n Familiarity with the principles of mission autonomy, unmanned systems, and the integration of multiple assets into cohesive operational frameworks\n US Salary Range\n $166,000 — $220,000 USD \n The salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business considerations. Highly competitive equity grants are included in the majority of full time offers; and are considered part of Anduril's total compensation package. Additionally, Anduril offers top-tier benefits for full-time employees, including:   \n  \n Benefits \n At Anduril, we invest in our people. Our comprehensive, competitive benefits package (available at little to no cost to employees) ensures you’re supported in health, recovery, and whatever comes next.  For more information, Explore Our Benefits . \n  \n \n Protecting Yourself from Recruitment Scams \n Anduril is committed to maintaining the integrity of our Talent acquisition process and the security of our candid","salary_min":166000,"salary_max":220000,"location":"Costa Mesa, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["fine-tuning","computer-vision","cloud","payments"],"apply_url":"https://boards.greenhouse.io/andurilindustries/jobs/5197631007?gh_jid=5197631007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-24T16:55:05Z","expires_at":"2026-09-29T13:37:16.382734Z","created_at":"2026-08-25T18:28:18.992397Z","updated_at":"2026-08-30T13:37:16.524244Z","company_name":"Anduril","company_slug":"anduril","company_logo_url":"https://www.google.com/s2/favicons?domain=anduril.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/d1680506-12cb-44c0-8cdd-d0bee0cdf4b4"},{"id":"ac071719-be76-48b4-a476-7a614bc11915","company_id":"2ca4efa5-edc2-4352-a597-ea27086e1e5b","title":"Machine Learning Engineer II, Ads - Response Prediction","slug":"machine-learning-engineer-ii-ads-response-prediction-3e47bda7","description":"We're transforming the grocery industry \n At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers. \n Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table.\n Instacart is a Flex First team \n There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work. \n Overview \n About the Role: \n As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart's ads systems. You will use machine learning to devise and refine solutions in crucial areas such as ads selection, ranking, bidding, and auction across all of Instacart’s consumer facing surfaces and Ads Ecosystems. You will actively contribute to initiatives, assisting in all stages of ML projects from the initial concept, through prototyping and experimentation, to final launch.\n About the Team: \n The Ads Response Prediction team owns systems, algorithms and ML models to ensure a relevant and engaging Ads experience to customers of all the platforms powered by Instacart. This includes search and exploration retrieval systems, Sequential Modeling and Generative Retrieval systems, LLM integrations, relevance models, pCTR models, bidding models and Foundation Models. The team optimizes for an efficient marketplace to ensure customers see ads that help them try new products/brands, advertisers boost their product sales for a good return on investment, and instacart generates the deserving revenue as well.\n  \n About the Job: \n \n Design, develop, and deploy machine learning solutions including data pipelines, model architectures and serving integrations to tackle practical challenges in the ads organization.\n Formulate and scope ambiguous modeling problems from first principles. Translate business observations (e.g., miss-calibration patterns, cold-start underperformance) into well-defined ML research directions with clear evaluation criteria.\n Collaborate closely with product managers, data scientists, and infrastructure engineers to deeply understand business needs and create impactful ML applications.\n Push the envelope on our operational efficiency by continually refining and advancing our algorithms and models.\n Publish and present findings internally. Contribute to the team’s culture of technical rigor through design reviews, paper sharing, and experiment retrospectives.\n \n  \n About You: \n Minimum Qualifications: \n \n Have a graduate degree (masters or PhD) in machine learning, statistics, computer science, information retrieval, or a closely related quantitative field.\n Have strong programming skills and fluency in data manipulation (SQL, Spark, Pandas) and Machine Learning (classical ML and Deep Learning) tools.\n Have strong analytical skills and problem-solving ability.\n Are a strong communicator who can collaborate with diverse stakeholders across all levels.\n \n  \n Preferred Qualifications: \n \n Have 1-2 years of industry experience using machine learning to solve real-world problems with large datasets.\n Knowledge of sequential modeling, Transformer architecture and Foundation Model.\n Familiarity with LLM integrations, agentic workflow and productivity tooling.\n Experience in building large scale online recommendation systems.\n \n #LI-Remote \n Instacart provides highly market-competitive compensation and benefits in each location where our employees work. This role is remote and the base pay range for a successful candidate is dependent on their permanent work location. Please review our Flex First remote work policy here . Currently, we are only hiring in the following provinces: Ontario, Alberta, British Columbia, and Nova Scotia.\n Offers may vary based on many factors, such as candidate experience and skills required for the role. Additionally, this role is eligible for a new hire equity grant as well as annual refresh grants. Please read more about our benefits offerings here .\n For Canadian based candidates, the base pay ranges for a successful candidate are listed below.\n CAN\n $154,000 — $162,500 CAD","salary_min":154000,"salary_max":162500,"location":"Remote (Canada)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"junior","tags":["agents","generative-ai","data-pipeline","fine-tuning","search","llm","deep-learning","machine-learning"],"apply_url":"https://instacart.careers/job/?gh_jid=8143263","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-20T19:14:19Z","expires_at":"2026-09-29T13:39:09.256769Z","created_at":"2026-08-25T18:28:59.889259Z","updated_at":"2026-08-30T13:39:09.402003Z","company_name":"Instacart","company_slug":"instacart","company_logo_url":"https://www.google.com/s2/favicons?domain=www.instacart.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/ac071719-be76-48b4-a476-7a614bc11915"},{"id":"ea9ed310-83a0-4da7-92f0-3500ba5c05a5","company_id":"2ca4efa5-edc2-4352-a597-ea27086e1e5b","title":"Senior Machine Learning Engineer, Digital Twin Platform","slug":"senior-machine-learning-engineer-digital-twin-platform-081ecc8d","description":"We're transforming the grocery industry \n At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers. \n Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table.\n Instacart is a Flex First team \n There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work. \n Overview \n The Digital Twin Platform team at Instacart is on a mission to understand exactly what is on store shelves at all times — bringing the precision and depth of a smart warehouse to every local grocery store across North America. Inventory estimates power some of the most critical products at Instacart, from search to logistics, and this team sits at the center of it all. Operating like a startup within a larger company, the team drives the end-to-end shelf data supply chain: ingesting data from retail partners, actively collecting novel inventory observations, developing sophisticated models, and integrating those outputs into live products at scale.\n We are looking for a Senior Machine Learning Engineer to help build the next generation of platforms for understanding, observing, and predicting inventory levels and in-store stocking dynamics in real time. In this role, you will develop machine learning models and deploy them into production systems, working in close collaboration with software engineers, computer vision engineers, product leads, and data scientists. If you're motivated by technically complex, high-impact problems and want to see your work shape how millions of people experience grocery shopping, this is the role for you.\n You can read more about some of the work this team is doing here:\n Introducing New Enterprise AI Solutions to Democratize AI for Grocers of All Sizes \n Instacart Acquires Arpalus to Advance Real-Time Shelf Intelligence \n About the Job \n \n Design, develop, and deploy machine learning models that power real-time understanding of in-store inventory levels and shelf stocking dynamics across thousands of retail locations at scale.\n Own the full ML lifecycle — from problem framing and data exploration through model training, evaluation, and production deployment — with a focus on quality, reliability, and measurable business impact.\n Collaborate cross-functionally with software engineers, computer vision engineers, data scientists, and product leads to bring cutting-edge technologies to the team and drive new product innovation.\n Contribute to building and evolving the core infrastructure of the Digital Twin Platform, including systems that ingest data from retail partners and actively collect novel inventory observations to feed the modeling pipeline.\n Help define the technical direction of an expanding modeling practice on a high-performance team that operates with startup speed — expect ambiguity, changing priorities, and the opportunity to make a significant mark on a problem space that is core to Instacart's business.\n \n About You \n Minimum Qualifications\n \n 5+ years of experience developing and deploying machine learning models in production environments.\n Strong proficiency in Python and experience with ML frameworks such as TensorFlow, PyTorch, or scikit-learn.\n Demonstrated experience with large-scale data pipelines and working with structured and unstructured data at scale.\n Experience with cloud infrastructure (AWS, GCP, or Azure) and familiarity with ML platform tooling for model training, versioning, and serving.\n Bachelor's degree or higher in Computer Science, Machine Learning, Statistics, or a related technical field, or equivalent practical experience.\n \n Preferred Qualifications\n \n Experience working on computer vision, inventory forecasting, demand sensing, or related spatial/temporal modeling problems.\n Familiarity with real-time inference systems and the architectural considerations of serving ML models in low-latency, high-throughput environments.\n Prior experience in a fast-paced, cross-functional environment where you have independently driven projects from conception through production with limited oversight.\n Exposure to retail, supply chain, or e-commerce domains and an understanding of how inventory data flows an","salary_min":206000,"salary_max":217500,"location":"Remote (Canada)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["pytorch","tensorflow","data-pipeline","computer-vision","fine-tuning","cloud","machine-learning"],"apply_url":"https://instacart.careers/job/?gh_jid=8143147","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-20T19:12:31Z","expires_at":"2026-09-29T13:39:09.843919Z","created_at":"2026-08-25T18:28:59.914166Z","updated_at":"2026-08-30T13:39:09.978396Z","company_name":"Instacart","company_slug":"instacart","company_logo_url":"https://www.google.com/s2/favicons?domain=www.instacart.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/ea9ed310-83a0-4da7-92f0-3500ba5c05a5"},{"id":"f4154f18-d97b-44e2-af31-0a213ace915e","company_id":"2ca4efa5-edc2-4352-a597-ea27086e1e5b","title":"Senior Machine Learning Engineer, Digital Twin Platform","slug":"senior-machine-learning-engineer-digital-twin-platform-85efdb23","description":"We're transforming the grocery industry \n At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers. \n Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table.\n Instacart is a Flex First team \n There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work. \n Overview \n The Digital Twin Platform team at Instacart is on a mission to understand exactly what is on store shelves at all times — bringing the precision and depth of a smart warehouse to every local grocery store across North America. Inventory estimates power some of the most critical products at Instacart, from search to logistics, and this team sits at the center of it all. Operating like a startup within a larger company, the team drives the end-to-end shelf data supply chain: ingesting data from retail partners, actively collecting novel inventory observations, developing sophisticated models, and integrating those outputs into live products at scale.\n We are looking for a Senior Machine Learning Engineer to help build the next generation of platforms for understanding, observing, and predicting inventory levels and in-store stocking dynamics in real time. In this role, you will develop machine learning models and deploy them into production systems, working in close collaboration with software engineers, computer vision engineers, product leads, and data scientists. If you're motivated by technically complex, high-impact problems and want to see your work shape how millions of people experience grocery shopping, this is the role for you.\n You can read more about some of the work this team is doing here:\n Introducing New Enterprise AI Solutions to Democratize AI for Grocers of All Sizes \n Instacart Acquires Arpalus to Advance Real-Time Shelf Intelligence \n About the Job \n \n Design, develop, and deploy machine learning models that power real-time understanding of in-store inventory levels and shelf stocking dynamics across thousands of retail locations at scale.\n Own the full ML lifecycle — from problem framing and data exploration through model training, evaluation, and production deployment — with a focus on quality, reliability, and measurable business impact.\n Collaborate cross-functionally with software engineers, computer vision engineers, data scientists, and product leads to bring cutting-edge technologies to the team and drive new product innovation.\n Contribute to building and evolving the core infrastructure of the Digital Twin Platform, including systems that ingest data from retail partners and actively collect novel inventory observations to feed the modeling pipeline.\n Help define the technical direction of an expanding modeling practice on a high-performance team that operates with startup speed — expect ambiguity, changing priorities, and the opportunity to make a significant mark on a problem space that is core to Instacart's business.\n \n About You \n Minimum Qualifications\n \n 5+ years of experience developing and deploying machine learning models in production environments.\n Strong proficiency in Python and experience with ML frameworks such as TensorFlow, PyTorch, or scikit-learn.\n Demonstrated experience with large-scale data pipelines and working with structured and unstructured data at scale.\n Experience with cloud infrastructure (AWS, GCP, or Azure) and familiarity with ML platform tooling for model training, versioning, and serving.\n Bachelor's degree or higher in Computer Science, Machine Learning, Statistics, or a related technical field, or equivalent practical experience.\n \n Preferred Qualifications\n \n Experience working on computer vision, inventory forecasting, demand sensing, or related spatial/temporal modeling problems.\n Familiarity with real-time inference systems and the architectural considerations of serving ML models in low-latency, high-throughput environments.\n Prior experience in a fast-paced, cross-functional environment where you have independently driven projects from conception through production with limited oversight.\n Exposure to retail, supply chain, or e-commerce domains and an understanding of how inventory data flows an","salary_min":201000,"salary_max":212000,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["cloud","pytorch","computer-vision","fine-tuning","tensorflow","data-pipeline","machine-learning"],"apply_url":"https://instacart.careers/job/?gh_jid=8143145","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-20T19:12:30Z","expires_at":"2026-09-29T13:39:09.752316Z","created_at":"2026-08-25T18:28:59.910126Z","updated_at":"2026-08-30T13:39:09.886675Z","company_name":"Instacart","company_slug":"instacart","company_logo_url":"https://www.google.com/s2/favicons?domain=www.instacart.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f4154f18-d97b-44e2-af31-0a213ace915e"},{"id":"42dee226-8fe7-4865-9781-15de96ca3cf0","company_id":"28040a6c-6f94-41a4-b15a-f2e4520188ff","title":"Forward Deployed Engineer","slug":"forward-deployed-engineer-a6d8ef8d","description":"About Dialpad Dialpad is the AI platform for customer experience, built to resolve customer problems in real time across voice and digital. Our AI agents learn from your best human agents and improve with every interaction, helping organizations understand their customers, deliver better experiences, increase operational efficiencies, and build a lasting competitive advantage. \n Unlike legacy systems built to route and answer, or standalone agentic bot vendors built to deflect, Dialpad was built to resolve. Our AI agents and human agents operate on a single platform with shared context, allowing Agentic AI to resolve issues, advance deals, and eliminate busywork through automation while seamlessly handing conversations to humans when needed, with full context preserved. \n Market-leading brands, including Randstad, Motorola Solutions, Netflix, the San Diego Padres, the Colorado Rockies Baseball Club, and Cal Athletics, trust Dialpad. Dialpad is backed by Andreessen Horowitz, GV, ICONIQ Capital, and T-Mobile. \n Being a Dialer At Dialpad, AI isn’t just a feature; it’s how our teams do their best work every day. We put powerful AI tools in every employee’s hands so they can move faster, think bigger, and achieve more. \n We believe every conversation matters. And we’ve built the platform that turns those conversations into insight and action, for our customers and ourselves. \n We look for people who are intensely curious and hold themselves to a high bar. Our ambition is significant, and achieving it requires a team that operates at the highest level. We seek individuals who embody our core traits: Scrappy, Curious, Optimistic, Persistent, and Empathetic . \n Your role The Forward Deployed Engineer (FDE) is a customer-embedded delivery engineer responsible for turning qualified AI opportunities into live, production-ready agent deployments. This role sits at the intersection of customer workflow design, applied AI engineering, enterprise integration, and production rollout. The core mission is to close the last-mile gap between platform capability and measurable customer outcomes by taking shaped opportunities from scoping through implementation and into the first production shift. \n You will work directly with customer stakeholders, internal delivery teams, and product partners to design, build, evaluate, deploy, and stabilize AI agents in real operating environments. This is a highly customer-facing, hands-on role for someone who can translate ambiguity into working systems and can move fluidly between technical depth and business impact. \n What you’ll do \n \n Convert prioritized use cases and pre-sales assumptions into a clear delivery scope, technical plan, and phased implementation approach. \n Work directly with customer technical and operational teams to map real workflows, exception paths, approvals, escalation rules, and human-in-the-loop controls. \n Design and build customer-specific AI agent solutions, including orchestration logic, prompt architecture, retrieval flows, workflow rules, and decision logic. \n Implement integrations with customer data sources, APIs, SaaS platforms, CRMs, ERPs, and enterprise knowledge systems so agents can perform real work end to end. \n Develop evaluation methods, guardrails, and acceptance criteria that measure agent usefulness, reliability, safety, task success, and production readiness. \n Run iterative testing, failure analysis, and performance tuning to improve quality, groundedness, latency, cost, and operational effectiveness. \n Prepare solutions for production by addressing access controls, observability, rollout sequencing, exception handling, fallback behavior, and support readiness. \n Lead deployment activities through initial go-live and first-shift stabilization, including rapid issue resolution, workflow adjustments, and customer-facing hypercare. \n Document reusable implementation patterns, operational runbooks, known failure modes, and recommended next improvements to support scale and repeatability. \n Feed field learnings back into Product, Engineering, and transformation teams so future deployments become faster, safer, and more repeatable. \n Detailed solution scoping after deal alignment. \n Customer-specific implementation and agent behavior design. \n Technical integration across enterprise systems and data sources. \n Evaluation, iterative quality improvement, and production hardening. \n Go-live readiness and first-shift production stabilization. \n Executive value framing before opportunity qualification. \n Broad transformation-roadmap design across multiple business units. \n Portfolio-level governance program management. \n Long-term managed-service ownership after the initial deployment phase, unless the service model explicitly extends the role. \n \n Skills you'll bring \n \n Experience in a high-impact, customer-facing technical role such as Forward Deployed Engineering, Solutions Architecture, Technical Consulting, Deployment Engineering, or AI","salary_min":112400,"salary_max":154400,"location":"Anywhere, US","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"mid","tags":["embeddings","llm","fine-tuning","agents"],"apply_url":"https://job-boards.greenhouse.io/dialpad/jobs/8734067002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-20T19:03:38Z","expires_at":"2026-09-29T13:50:50.077182Z","created_at":"2026-08-25T19:52:34.272555Z","updated_at":"2026-08-30T13:50:50.205965Z","company_name":"Dialpad","company_slug":"dialpad","company_logo_url":"https://www.google.com/s2/favicons?domain=dialpad.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/42dee226-8fe7-4865-9781-15de96ca3cf0"}],"page":1,"per_page":20,"total":871,"total_is_exact":true,"total_pages":44}