{"has_next":true,"jobs":[{"id":"48720738-0f4b-483d-9739-14039ae457d0","company_id":"a0000000-0000-0000-0000-000000000001","title":"Research Engineer, Performance RL","slug":"research-engineer-performance-rl-2f0da25a","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 the RL Teams \n Our Reinforcement Learning teams lead Anthropic's reinforcement learning research and development, playing a critical role in advancing our AI systems. We've contributed to all Claude models, with significant impacts on the autonomy and coding capabilities of Claude Sonnet 4.6 and Opus 4.6. Our work spans several key areas:\n \n \n Developing systems that enable models to use computers effectively\n \n Advancing code generation through reinforcement learning\n \n Pioneering fundamental RL research for large language models\n \n Building scalable RL infrastructure and training methodologies\n \n Enhancing model reasoning capabilities\n \n We collaborate closely with Anthropic's alignment and frontier red teams to ensure our systems are both capable and safe. We partner with the applied production training team to bring research innovations into deployed models, and are dedicated to implement our research at scale. Our Reinforcement Learning teams sit at the intersection of cutting-edge research and engineering excellence, with a deep commitment to building high-quality, scalable systems that push the boundaries of what AI can accomplish.\n About the Role \n We're hiring for the Code RL team within the RL organization. As a Research Engineer, you'll advance our models' ability to safely write correct, fast code for accelerators.\n You'll need to know accelerator performance well to turn it into tasks and signals models can learn from. Specifically, you will:\n \n \n Invent, design and implement RL environments and evaluations.\n \n Conduct experiments and shape our research roadmap.\n \n Deliver your work into training runs.\n \n Collaborate with other researchers, engineers, and performance engineering specialists across and outside Anthropic.\n \n You may be a good fit if you:\n \n \n Have expertise with accelerators (CUDA, ROCm, Triton, Pallas), ML framework programming (JAX or PyTorch).\n \n Have worked across the stack – kernels, model code, distributed systems.\n \n Know how to balance research exploration with engineering implementation.\n \n Are passionate about AI's potential and committed to developing safe and beneficial systems.\n \n Strong candidates may also have:\n \n \n Experience with reinforcement learning.\n \n Experience porting ML workloads between different types of accelerators.\n \n Familiarity with LLM training methodologies.\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 $350,000 — $850,000 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 yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team. Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a ","salary_min":350000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","job_type":"full-time","experience_level":"principal","tags":["reinforcement-learning","gpu","code-generation","distributed-systems","search","jax","fine-tuning","llm"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5160330008","is_featured":true,"is_sticky":true,"status":"active","published_at":"2026-03-23T16:27:59Z","expires_at":"2026-06-29T14:00:21.52187Z","created_at":"2026-04-13T09:36:00.086246Z","updated_at":"2026-05-30T14:00:21.633054Z","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/48720738-0f4b-483d-9739-14039ae457d0"},{"id":"f47b2b52-9138-4056-a197-783873a96c39","company_id":"f5ee7284-a657-4da2-b351-cb806a3681cd","title":"Member of Technical Staff - Voice Model","slug":"member-of-technical-staff-voice-model-5b5f6cb9","description":"ABOUT xAI \n xAI’s mission is to create AI systems that can accurately understand the universe and aid humanity in its pursuit of knowledge. Our team is small, highly motivated, and focused on engineering excellence. This organization is for individuals who appreciate challenging themselves and thrive on curiosity. We operate with a flat organizational structure. All employees are expected to be hands-on and to contribute directly to the company’s mission. Leadership is given to those who show initiative and consistently deliver excellence. Work ethic and strong prioritization skills are important. All employees are expected to have strong communication skills. They should be able to concisely and accurately share knowledge with their teammates. \n ABOUT THE ROLE:\n You will join the Grok Voice Model team to help build the world’s best voice AI. We deliver smooth, natural, low-latency spoken interactions — expressive, multilingual, and reliable across devices and real-time scenarios. We own the full training pipeline: massive data curation, premium audio processing, frontier speech-language pre-training, and intensive post-training to push quality, speed, and stability to the limit.\n Our goal: make talking to AI feel like conversing with the most charming, kind, and knowledgeable person imaginable. We’re seeking exceptionally smart, execution-oriented engineers to help us get there.\n RESPONSIBILITIES:\n \n Design and execute large-scale speech data curation and processing pipelines, including collection of diverse real-world audio, synthetic data generation, and automated annotation workflows to enable high-quality model training and evaluation.\n Work on pre-training and post-training of speech-language models, with targeted enhancements through supervised fine-tuning, reinforcement learning, and other techniques to ensure Grok Voice responses are accurate, factually grounded, natural and idiomatic in spoken style, conversational in tone, and fluent across multiple languages.\n Build and iterate a comprehensive evaluation framework covering objective metrics (accuracy, quality, latency, expressiveness), human preference studies, content factuality assessments, real-time interaction quality, and experimentation infrastructure to measure and improve performance.\n Work closely with product teams to integrate voice models into applications and real-time environments, define spoken interaction specifications, and handle the full lifecycle from prototype to global-scale deployment for stable, low-latency, delightful voice experiences.\n \n BASIC QUALIFICATIONS:\n \n Python expert with deep proficiency in writing clean, efficient code for AI/ML systems.\n Hands-on experience processing large-scale datasets using tools like Spark and Ray for cleaning, augmentation, and feature extraction.\n Proficiency in pre-training and post-training speech-language models using JAX/PyTorch, including supervised fine-tuning, reinforcement learning, and optimizations for accuracy, factuality, natural spoken style, detail, and multilingual fluency.\n Ability to set up and run rigorous evaluation pipelines: objective metrics, human preference studies, content factuality checks, and iterative A/B testing to drive model improvements.\n Experience building or working with large-scale distributed training and inference systems on Kubernetes.\n Proactive, self-driven attitude — ready to grind in a fast-paced, high-caliber team to deliver outstanding voice AI experiences.\n \n COMPENSATION AND BENEFITS:\n $150,000 - $450,000 USD\n Base salary is just one part of our total rewards package at xAI, which also includes equity, comprehensive medical, vision, and dental coverage, access to a 401(k) retirement plan, short \u0026 long-term disability insurance, life insurance, and various other discounts and perks.\n xAI is an equal opportunity employer. For details on data processing, view our  Recruitment Privacy Notice .","salary_min":150000,"salary_max":450000,"location":"Palo Alto, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["speech","reinforcement-learning","pre-training","pytorch","fine-tuning","distributed-systems"],"apply_url":"https://job-boards.greenhouse.io/xai/jobs/5051966007","is_featured":true,"is_sticky":false,"status":"active","published_at":"2026-03-16T20:39:18Z","expires_at":"2026-06-29T14:02:58.935925Z","created_at":"2026-04-13T09:38:43.3144Z","updated_at":"2026-05-30T14:02:59.041832Z","company_name":"xAI","company_slug":"xai","company_logo_url":"https://www.google.com/s2/favicons?domain=x.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f47b2b52-9138-4056-a197-783873a96c39"},{"id":"fd4226af-9faa-4819-8327-113cce284a3e","company_id":"a355eb2f-63c3-4c0a-803d-bc2d8312b6d8","title":"Software Engineer, Delivery / CD","slug":"software-engineer-delivery-cd-ac48f409","description":"About the Role\n\nThe Engineering Acceleration Delivery / Continuous Deployment team builds and operates the systems that safely ship OpenAI’s infrastructure and product code to production.\n\nWe own the deployment platform, release pipelines, and rollout safety mechanisms that allow engineers across OpenAI to deploy changes rapidly while minimizing operational risk. Our mission is to make production deployments fast, safe, and increasingly autonomous.\n\nThis role sits at the intersection of developer productivity, distributed systems reliability, and large-scale infrastructure orchestration.\n\n\n\nIn This Role, You Will\n\n - Design and build continuous deployment infrastructure that safely rolls out changes across dozens of Kubernetes clusters and global regions.\n\n - Develop systems for progressive delivery, including canary releases, staged rollouts, and automated rollback.\n\n - Improve engineering velocity by reducing friction in the release pipeline and automating manual operational workflows.\n\n - Work with product and infrastructure teams to ensure their services are deployable, observable, and resilient at scale.\n\n - Implement and evolve deployment methodologies such as GitOps, infrastructure-as-code, and progressive delivery patterns.\n\n - Build systems that automatically evaluate deployment health using metrics, logs, traces, and alerts to detect regressions and trigger safe rollbacks.\n\n - Build systems that support agent-assisted or autonomous deployment workflows using modern AI tooling.\n   \n   \n\nTechnologies commonly used in this environment include:\n\n\n\n - Kubernetes for large-scale container orchestration and runtime infrastructure\n\n - Python and FastAPI for internal services\n\n - Terraform for infrastructure as code\n\n - GitOps-based deployment workflows (e.g., ArgoCD, Flux, or similar systems)\n\n - Buildkite for CI orchestration\n   \n\nYou may be a strong fit if you:\n\n - Have worked with Kubernetes-based deployment systems at scale\n\n - Have experience building or operating continuous deployment platforms\n\n - Are familiar with GitOps tooling such as ArgoCD or Flux\n\n - Are excited about building AI-assisted systems and agents that intelligently shepherd software changes from commit to safe production rollout.\n\n - Care deeply about safe production rollouts and minimizing blast radius\n\n - Enjoy building internal platforms that improve developer productivity across the organization\n   \n   \n\nCompensation\n\n$230K – $490K + Offers Equity\n\n\n\n\n\nAbout OpenAI\n\nOpenAI is an AI research and deployment company dedicated to ensuring that general-purpose artificial intelligence benefits all of humanity. We push the boundaries of the capabilities of AI systems and seek to safely deploy them to the world through our products. AI is an extremely powerful tool that must be created with safety and human needs at its core, and to achieve our mission, we must encompass and value the many different perspectives, voices, and experiences that form the full spectrum of humanity. \n\nWe are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic.\n\nFor additional information, please see OpenAI’s Affirmative Action and Equal Employment Opportunity Policy Statement https://cdn.openai.com/policies/eeo-policy-statement.pdf.\n\nBackground checks for applicants will be administered in accordance with applicable law, and qualified applicants with arrest or conviction records will be considered for employment consistent with those laws, including the San Francisco Fair Chance Ordinance, the Los Angeles County Fair Chance Ordinance for Employers, and the California Fair Chance Act, for US-based candidates. For unincorporated Los Angeles County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: protect computer hardware entrusted to you from theft, loss or damage; return all computer hardware in your possession (including the data contained therein) upon termination of employment or end of assignment; and maintain the confidentiality of proprietary, confidential, and non-public information. In addition, job duties require access to secure and protected information technology systems and related data security obligations.\n\nTo notify OpenAI that you believe this job posting is non-compliant, please submit a report through this form https://form.asana.com/?d=57018692298241\u0026k=5MqR40fZd7jlxVUh5J-UeA. No response will be provided to inquiries unrelated to job posting compliance.\n\nWe are committed to providing reasonable accommodations to applicants with disabilities, and requests can be made via this link https://form.asana.com/?k=bQ7w9h3iexRlicUdWRiwvg\u0026d=57018692298241.\n\nOpenAI Global Applicant Privacy P","salary_min":230000,"salary_max":490000,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["distributed-systems","infrastructure"],"apply_url":"https://jobs.ashbyhq.com/openai/e14fc37c-7ae5-4a6b-ba0d-a36860cf9bb2/application","is_featured":true,"is_sticky":false,"status":"active","published_at":"2026-05-04T18:54:22.168Z","expires_at":"2026-06-29T14:00:50.232199Z","created_at":"2026-04-13T09:36:32.989672Z","updated_at":"2026-05-30T14:00:50.33833Z","company_name":"OpenAI","company_slug":"openai","company_logo_url":"https://www.google.com/s2/favicons?domain=openai.com\u0026sz=128","quality_score":85,"url":"https://aidevboard.com/job/fd4226af-9faa-4819-8327-113cce284a3e"},{"id":"a3d16455-f42f-4915-8723-2d023a5b665b","company_id":"2ca4efa5-edc2-4352-a597-ea27086e1e5b","title":"Senior Software Engineer II, AI Labs \u0026 Foundations","slug":"senior-software-engineer-ii-ai-labs-foundations-e74eb4cd","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 Join Instacart's mission to transform grocery shopping through frontier AI. As a Senior Software Engineer on AI Labs \u0026 Foundations, you will design, build, and operate the high-scale production systems that power our most ambitious AI experiences—from Cart Assistant, our conversational shopping agent, to voice AI interactions and beyond. This is a high-impact opportunity to work at the intersection of robust software engineering and cutting-edge production AI/ML, directly shaping products used by millions of customers every day.\n We are hiring a Senior Software Engineer who will participate in the design and delivery of production AI systems, identify high-leverage technical opportunities, and contribute hands-on to AI-native products across Instacart's platform. We value bottom-up ideas, high engineering quality, and close partnership with Product, Data Science, ML, and Infrastructure teams. If you enjoy inventing, navigating ambiguity, prototyping fast, and turning wild ideas into real, scalable products, this is the team for you.\n AI Labs \u0026 Foundations sits at the intersection of frontier AI research and production engineering. Our portfolio spans the full stack of AI innovation at Instacart, including building and launching Cart Assistant, pioneering voice AI interactions, and constructing the foundational systems that power these cutting-edge experiences. We are a fast-moving, collaborative team that thrives on 0-to-1 thinking, shares learnings openly, and ships with urgency by prototyping fast and testing rigorously.\n About the Job\n \n Design, build, and operate production AI-powered systems and agentic experiences (including Cart Assistant and voice AI) that directly impact how millions of customers shop.\n Build foundational systems for cutting-edge AI experiences, ranging from embedding infrastructure and voice AI pipelines, to client facing components and integrations, by prototyping bold ideas and productizing what works.\n Integrate foundation models via APIs and open-source frameworks; apply techniques like retrieval-augmented generation and vector search where appropriate.\n Own projects end-to-end: requirements, technical design, implementation, testing, deployment, observability, and iterative improvement focused on reliability, latency, and cost efficiency.\n Collaborate with cross-functional partners in product, design, data science, and infrastructure to ship AI features end-to-end.\n Drive engineering excellence, including thoughtful system design, rigorous code review, and technical leadership that includes defining and promoting best practices for AI/ML production engineering across the team.\n \n About You\n Minimum Qualifications: \n \n Proven senior software engineer who has built, shipped, and operated production systems at scale. You make architectural calls, own what you build, and deliver through ambiguity.\n Hands-on experience with AI or ML in production. You've shipped LLM-powered features or integrated foundation model APIs into a live product, demonstrating the necessary expertise at the intersection of robust software engineering and deep production ML.\n Experience owning services end-to-end, including CI/CD, automated testing, observability (logging, metrics, tracing), and on-call participation.\n Strong communicator who partners well across disciplines - you want to get to the right answer, not just defend the first one.\n Excitement and ability to leverage cutting-edge development tools, including AI assistance (e.g., Copilot, Cursor, Claude), to maximize velocity.\n \n Preferred Qualifications: \n \n 5 to 8+ years of industry experience.\n A track record of 0-to-1 work taking unconventional ideas from prototype through rapid iteration to production.\n Experience building conversational agents, multi-turn dialogue systems, or agentic LLM applications.\n Exp","salary_min":192000,"salary_max":202000,"location":"Remote (US)","workplace":"remote","job_type":"full-time","experience_level":"senior","tags":["cloud","fine-tuning","code-generation","generative-ai","llm","distributed-systems","agents","speech"],"apply_url":"https://instacart.careers/job/?gh_jid=7951041","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-29T22:43:14Z","expires_at":"2026-06-29T14:08:42.057285Z","created_at":"2026-05-30T14:08:42.180879Z","updated_at":"2026-05-30T14:08:42.180879Z","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/a3d16455-f42f-4915-8723-2d023a5b665b"},{"id":"09d0acb5-52de-4a76-88c6-0eb844785025","company_id":"053355fc-0162-4bb9-b414-cbf7679ee9c8","title":"Research Scientist - RL Training","slug":"research-scientist-rl-training-ffdbae39","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 between 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 We're looking for a Research Scientist to work on reinforcement learning for training and aligning large language models. This is a foundational research role focused on one of the most consequential open data problems in AI: how to generate the data, reward signals, and training procedures that steer LLM behavior in reliable and generalizable directions — and a core capability that directly differentiates Snorkel's data-as-a-service offering. \n You'll work closely with Snorkel's research, engineering, and delivery teams to advance our RL data capabilities — translating research ideas into the preference datasets, reward models, and RL-ready corpora we produce for frontier AI labs, and contributing to a research agenda that is central to Snorkel's long-term differentiation as a provider of bespoke training data. \n MAIN RESPONSIBILITIES  \n \n Research and implement reinforcement learning techniques — including GRPO, RLHF, RLAIF, DPO, and reward modeling — and translate them into data products (preference datasets, reward signals, verifiable rewards) that customers can use to train and fine-tune large language models. \n Design and build data pipelines that generate high-quality training signal for RL workflows, including AI-assisted data annotation and curation data pipelines to improve model generalization to unseen benchmarks . \n Prototype and iterate on end-to-end RL training recipes that inform what data Snorkel ships as part of its data-as-a-service deliveries. \n Work closely with research scientists, ML engineers, and delivery teams to translate RL research into customer-ready data products.\n Stay current with the latest developments in large-scale muli-node LLM training, alignment research, and scalable RL methods (on complex environments such as Terminal-Bench), bringing relevant advances into Snorkel's data-as-a-service approach.\n Contribute to Snorkel's research publications and internal knowledge base in RL and model training.\n \n PREFERRED QUALIFICATIONS  \n \n Deep expertise in reinforcement learning from human or AI feedback, reward modeling and credit attribution ideally with a clear perspective on what data makes these techniques work. \n Experience training or fine-tuning 30B+ large language models at scale, including familiarity with distributed training infrastructure. \n Strong proficiency in Python and ML frameworks, especially PyTorch and HuggingFace and hands-on experience with RL frameworks such as Verl and SkyRL. \n Solid software engineering fundamentals — you can build research prototypes that others can run, extend, and integrate into data production workflows. \n Familiarity with ML infrastructure and cloud platforms and tools (AWS, GCP, Kubernetes, Slurm, etc.); experience with large-scale RL training pipelines a strong plus. \n Comfort operating in a high-iteration environment with open-ended research questions and shifting, customer-driven technical constraints. \n Ph.D. in machine learning, reinforcement learning, or a related field strongly preferred; exceptional industry experience considered. \n Salary Range \n $200,000 — $325,000 USD \n Be Your Best at Snorkel \n Joining Snorkel AI means becoming part of a company that has market proven solutions, robust funding, and is scaling rapidly—offering a unique combination of stability and the excitement of high growth. As a member of our team, you’ll have meaningful opportunities to shape priorities and initiatives, influence key strategic decisions, and directly impact our ongoing success. Whether you’re looking to deepen your technical expertise, explore leadership opportunities, or learn new skills across multiple functions, you’re fully supported in building your career in an environment designed for growth, learning, and shared success.\n Snorkel AI is proud to be an Equal Employment Opportunity employer and is committed to building a team that represents a variety of backgrounds, perspectives, and skills. Snorkel AI embraces diversity and provides equal employment opportunities to all employees and applicants for employment. Snorkel AI prohibits discrimination and haras","salary_min":200000,"salary_max":325000,"location":"Redwood City, CA","workplace":"remote","job_type":"full-time","experience_level":"senior","tags":["alignment","distributed-systems","pytorch","fine-tuning","generative-ai","data-pipeline","llm","reinforcement-learning"],"apply_url":"https://job-boards.greenhouse.io/snorkelai/jobs/6009496004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-29T21:22:40Z","expires_at":"2026-06-29T14:03:05.747327Z","created_at":"2026-05-30T14:03:05.857458Z","updated_at":"2026-05-30T14:03:05.857458Z","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/09d0acb5-52de-4a76-88c6-0eb844785025"},{"id":"a6cf2026-eaea-495b-8177-860a11bedb45","company_id":"168d43fe-0922-420c-9743-59e0a899fd9d","title":"Data Scientist","slug":"data-scientist-24024c78","description":"A Career with Point72’s Technology Team\n As Point72 reimagines the future of investing, our Technology team is constantly evolving our firm’s IT infrastructure and engineering capabilities, positioning us at the forefront of a rapidly evolving technology landscape. We’re a team of experts who experiment and work to discover new ways to harness open-source solutions, modern cloud architectures, and sophisticated Artificial Intelligence (AI) solutions, while embracing enterprise agile methodologies. Our commitment to building and innovating in the AI space provides the framework intended to drive smarter decision making and enhance how we build and operate our platforms and applications.\n As a member of Point72’s Technology team, we encourage and support your professional development from day one—helping you advance your technical skills, contribute innovative ideas, and satisfy your own intellectual curiosity—all while delivering real business impact for our multi-billion-dollar global business.\n  \n What you’ll do\n \n Lead the development and deployment of advanced models and algorithms that turn complex data into actionable insights to influence decisions across the organization\n Build and champion the rollout of a technology insights product, setting clear service standards, aligning stakeholders, and establishing transparent metrics to measure impact and drive adoption\n Design and maintain a centralized analytics platform that unifies key performance indicators, satisfaction scores, and operational metrics into intuitive dashboards for leadership\n Develop automated data pipelines and validation processes to gather, clean, and prepare large sets of structured and unstructured data for modeling and analysis\n Partner with data engineers, analysts, and business partners to translate business challenges into scalable, production-ready data solutions and shared standards\n Create reports and drill-down analyses that highlight service health, enable targeted action planning, and support proactive management\n Monitor and analyze performance across service quality, project manager satisfaction, efficiency, operational risk, and cost, highlighting trade-offs and providing strategic recommendations\n Use historical trend analysis and experimentation to uncover recurring issues, measure the impact of corrective actions, and drive continuous improvement\n Integrate third-party data sources and application programming interfaces into the analytics ecosystem to expand capabilities and enrich models\n Explore and implement modern cloud-native and distributed computing tools and methodologies to improve scalability, reliability, and reproducibility\n \n  \n What’s required\n \n 5–10 years of professional experience in data science or a closely related field in financial services or technology environments\n Bachelor's or master's degree in computer science, data science, statistics, engineering, or a related technical discipline\n Deep expertise in statistical modeling, machine learning, and data mining using Python, R, or similar programming languages\n Demonstrable experience with cloud-based analytics platforms, such as Amazon Web Services (AWS), and distributed computing frameworks, such as Spark or Databricks\n Strong skills in data wrangling, feature engineering, data quality management, and production data pipeline design\n Experience designing and implementing performance management systems, dashboards, or service excellence frameworks that inform leadership decisions\n Solid understanding of data architecture, data governance, reproducible research practices, and model monitoring in production\n Experience with version control systems—such as Git—continuous integration and delivery workflows, and modern workflow orchestration tools\n Proven ability to communicate complex analyses clearly to technical and non-technical stakeholders and to collaborate effectively in fast-paced, high-stakes environments\n Commitment to the highest ethical standards\n \n  \n We take care of our people\n We invest in our people, their careers, their health, and their well-being. When you work here, we provide:\n \n Fully-paid health care benefits\n Generous parental and family leave policies\n Volunteer opportunities\n Support for employee-led affinity groups representing women, people of color and the LGBT+ community\n Mental and physical wellness programs\n Tuition assistance\n A 401(k) savings program with an employer match and more\n \n  \n About Point72\n Point72 is a leading global alternative investment firm led by Steven A. Cohen. Building on more than 30 years of investing experience, Point72 seeks to deliver superior returns for its investors through fundamental and systematic investing strategies across asset classes and geographies. We aim to attract and retain the industry’s brightest talent by cultivating an investor-led culture and committing to our people’s long-term growth. For more information, visit  https://point72.com","salary_min":200000,"salary_max":300000,"location":"New York, NY","workplace":"onsite","job_type":"full-time","experience_level":"principal","tags":["distributed-systems","data-pipeline","mlops","data-science"],"apply_url":"https://boards.greenhouse.io/point72/jobs/8568268002?gh_jid=8568268002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-29T16:17:45Z","expires_at":"2026-06-29T14:11:58.685291Z","created_at":"2026-05-30T14:11:58.799327Z","updated_at":"2026-05-30T14:11:58.799327Z","company_name":"Point72","company_slug":"point72","company_logo_url":"https://www.google.com/s2/favicons?domain=point72.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a6cf2026-eaea-495b-8177-860a11bedb45"},{"id":"73600478-6692-47ce-be77-2aebfb5bb4a2","company_id":"82d2abc2-444c-4d89-9646-4739e72d700d","title":"Machine Learning Engineer","slug":"machine-learning-engineer-5aefaff6","description":"About Checkr Checkr is building the data platform to power safe and fair decisions. Over 140,000 companies and millions of people rely on Checkr for AI verification in the moments that matter most: getting a new job, a new place to live, a car ride, childcare, even a date. Customers include Uber, Pennymac, Airbnb, Doordash, Amazon, and Anthropic. We’re a team that thrives on solving complex problems with innovative solutions that advance our mission. Checkr is recognized on Forbes Cloud 100 2025 List and is a Y Combinator 2024 Breakthrough Company .\n About the team/role \n We’re hiring an ML Engineer (P2) to build and ship the AI systems that power Checkr’s core products. This role sits on the ML team inside Checkr’s Data \u0026 ML organization within Engineering.\n Checkr runs millions of background checks a year. The ML team builds the systems that make those checks faster, more accurate, and cheaper to operate: document processing, charge classification, entity resolution, and in-product intelligence. These are production services that Product Engineering depends on daily.\n This is not a research role or a notebook role. You’ll own ML services end-to-end: design them, code them, deploy them, monitor them. We need someone who writes production software, builds with LLMs and APIs as first-class tools, and can tell the difference between working code and AI slop. If you’ve spent the last few years building AI-native software and you care deeply about engineering craft, we want to talk.\n This role sits in the central Data \u0026 ML team within the Engineering organization. You will partner daily with Product Engineering, Product, and cross-functional teams. You’ll also contribute to Checkr’s broader AI strategy, including our initiative to deploy our agentic fleet and build scalable context with our semantic layer.\n We are looking for someone based in San Francisco who has built ML systems in fast-moving, impact-first environments. Less process, more shipping. Less paperwork, more results.\n  \n What you’ll do \n \n Build and deploy ML/AI services. Design, develop, and ship ML models and AI systems that Product Engineering teams rely on. You write the model code, the API layer, the monitoring, and the tests. Not notebooks; production services.\n Design with LLMs and APIs. Use LLM APIs (OpenAI, Anthropic, etc.) as building blocks in production systems. You know when to call an LLM, when to fine-tune, when to use a classical model, and when to write a rule. You think about cost, latency, and quality together.\n Ship production software. Write clean, well-structured code with solid OOP, proper abstractions, error handling, and tests. Your code gets reviewed by SWEs and passes. CI/CD is how you work, not something you bolt on at the end.\n Partner with product and engineering. Translate business problems into ML solutions. Define API contracts with product engineers. Explain your approach clearly to non-ML partners and leave the room with alignment, not confusion.\n Evaluate and iterate fast. Build evaluation frameworks, run experiments, and make data-driven decisions about model and system performance. Ship and iterate; don’t wait for perfect.\n Ship AI-powered workflows. Put AI to work on your own processes: automate pipelines, build agentic workflows, and contribute reusable skills and context to Checkr’s agentic platform. The expectation is that our teams operate AI-first.\n \n What you bring \n \n A Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related technical field, or equivalent depth from experience\n 4+ years building software professionally, with at least 2 of those building ML systems that run in production\n Strong Python fluency; you write clean, testable, well-structured code with solid OOP instincts. Not scripts; software\n Hands-on experience using LLM APIs in production systems: prompt engineering, structured outputs, function calling, cost management, and evaluation\n You’ve built and maintained APIs, worked with CI/CD pipelines, and shipped code that other engineers depend on\n Comfortable with distributed systems concepts: queues, async processing, caching, horizontal scaling\n Experience with NLP tasks in production: classification, extraction, entity resolution, summarization\n Comfort with and enthusiasm for AI-assisted workflows; experience using LLMs, code-generation tools, or agentic systems in production or operational contexts is a strong signal\n You can evaluate tradeoffs: fine-tune vs. prompt, hosted vs. self-deployed, classical ML vs. LLM, rule vs. model\n Strong communication skills; you explain technical decisions clearly to engineers and non-engineers alike, without hiding behind jargon\n You use AI tools (Copilot, Claude, etc.) to move faster, but you understand every line they produce. You can spot AI slop and you don’t ship it\n An A-player mindset with a strong bias for action: you raise the bar, move with urgency, stay resilient through ambiguity, and t","salary_min":168000,"salary_max":198000,"location":"San Francisco, CA","workplace":"onsite","job_type":"full-time","experience_level":"mid","tags":["nlp","code-generation","mlops","agents","payments","legal","distributed-systems","llm"],"apply_url":"https://job-boards.greenhouse.io/checkr/jobs/7966920","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-29T15:17:56Z","expires_at":"2026-06-29T14:10:31.076983Z","created_at":"2026-05-30T14:10:31.19215Z","updated_at":"2026-05-30T14:10:31.19215Z","company_name":"Checkr","company_slug":"checkr","company_logo_url":"https://www.google.com/s2/favicons?domain=checkr.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/73600478-6692-47ce-be77-2aebfb5bb4a2"},{"id":"530f705a-007a-497f-9f62-9a6e196ea9ad","company_id":"1df860e2-0800-48ea-81af-7121965be17a","title":"Engineering Manager - Machine Learning","slug":"engineering-manager-machine-learning-e1742de5","description":"Your work will change lives. Including your own. \n \n The Impact You’ll Make \n You will lead a team working to build, scale, and optimize the machine learning infrastructure that powers Recursion's drug discovery platform. From model training pipelines to production deployment systems, to agent infrastructure and Large Language Models, you will ensure our ML models can operate at massive scale across our supercomputing infrastructure, both on prem and in the cloud. You will work cross-functionally across ML engineering, data science, and research teams to translate requirements into robust, scalable ML infrastructure solutions.\n In This Role You Will: \n \n Enable AI/ML, LLM, and Agentic Systems teams for scale - The ML infrastructure team is responsible for building and operating platforms that allow data scientists and ML engineers to train, deploy, and monitor models across Recursion's massive datasets. With billions of compounds, 30+ petabytes of experimental data, and complex deep learning workloads, your team enables everything from automated compound screening models to clinical trial prediction systems. You will work closely with researchers and ML engineers to understand their infrastructure needs and build scalable solutions for model development, training, and deployment.\n Act as a mentor, coach, and sponsor - You will share your technical, leadership and managerial skills in MLOps, distributed computing, and infrastructure engineering, delivering impact, learning, and growth across teams at Recursion. We believe that the best work comes from working across organizational boundaries and you will have opportunities to partner with ML research, platform engineering, and business teams.\n Enable a model-driven culture - Machine learning is at the core of everything we do. You will work with stakeholders across the business to ensure our ML infrastructure supports rapid experimentation, reliable model deployment, and continuous improvement. Problems you will work on could range from optimizing GPU cluster utilization to implementing Agentic orchestration and establishing company-wide MLOps standards\n \n The Team You’ll Join: \n You'll be part of a group of technical leaders who work together on the craft of engineering leadership as well as debate ML system architecture, MLOps patterns, and infrastructure optimization strategies. We all work better when we have the support of those around us and are learning together to solve complex problems around model scalability, deployment reliability, and infrastructure efficiency across our teams. You will report to the Executive Director of Engineering who broadly oversees Cloud Infrastructure, High Performance Compute and Machine Learning Infrastructure space.\n The Experience You Will Need: \n \n Experience in a hands-on technical role as a tech lead or a manager with a focus on infrastructure, MLOps and distributed systems. Excitement for deeply engaging in technical details with your team around machine learning, orchestration and agentic systems.\n A people-first mindset. We deliver in a way that prioritizes supporting our coworkers in their growth and experience and understand how Conway's Law shapes our ML system outcomes.\n Demonstrated past record of learning from and teaching peers in areas of ML infrastructure, model deployment, distributed compute, GPU optimization, and MLOps system architecture\n Excitement to learn parts of our ML tech stack that you might not already know. Our current ML infrastructure includes: Python, PyTorch, Docker, Kubernetes, Ray, Weights \u0026 Biases, Prefect, BigQuery, Postgres, GCP, CUDA, and various model serving frameworks.\n Fluency in life sciences or drug discovery is a plus but not required to be considered.\n \n Working Location \u0026 Compensation: \n This is a remote position based in Toronto, Canada. \n At Recursion, we believe that every employee should be compensated fairly. Based on the skill and level of experience required for this role, the estimated current annual base range for this role is $210,070 to $282,851 (CAD) . You will also be eligible for an annual bonus and equity compensation, as well as a comprehensive benefits package. \n #LI-EP1\n The Values We Hope You Share: \n \n We act boldly with integrity. We are unconstrained in our thinking, take calculated risks, and push boundaries, but never at the expense of ethics, science, or trust. \n We care deeply and engage directly. Caring means holding a deep sense of responsibility and respect - showing up, speaking honestly, and taking action.\n We learn actively and adapt rapidly. Progress comes from doing. We experiment, test, and refine, embracing iteration over perfection.\n We move with urgency because patients are waiting. Speed isn’t about rushing but about moving the needle every day.\n We take ownership and accountability. Through ownership and accountability, we enable trust and autonomy—leaders take accountability for decisive action, and teams own outcomes ","salary_min":210070,"salary_max":282851,"location":"Toronto, Canada","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["distributed-systems","agents","mlops","gpu","healthcare","deep-learning","pytorch","llm"],"apply_url":"https://job-boards.greenhouse.io/recursionpharmaceuticals/jobs/7961536","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-29T14:56:14Z","expires_at":"2026-06-29T14:07:04.607932Z","created_at":"2026-05-30T14:07:04.722791Z","updated_at":"2026-05-30T14:07:04.722791Z","company_name":"Recursion","company_slug":"recursion","company_logo_url":"https://www.google.com/s2/favicons?domain=recursion.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/530f705a-007a-497f-9f62-9a6e196ea9ad"},{"id":"58a5e82c-4cbd-49e1-9ce2-45a7b213b0a9","company_id":"1df860e2-0800-48ea-81af-7121965be17a","title":"Engineering Manager - Machine Learning","slug":"engineering-manager-machine-learning-288c8ba8","description":"Your work will change lives. Including your own. \n \n The Impact You’ll Make \n You will lead a team working to build, scale, and optimize the machine learning infrastructure that powers Recursion's drug discovery platform. From model training pipelines to production deployment systems, to agent infrastructure and Large Language Models, you will ensure our ML models can operate at massive scale across our supercomputing infrastructure, both on prem and in the cloud. You will work cross-functionally across ML engineering, data science, and research teams to translate requirements into robust, scalable ML infrastructure solutions.\n In This Role You Will: \n \n Enable AI/ML, LLM, and Agentic Systems teams for scale - The ML infrastructure team is responsible for building and operating platforms that allow data scientists and ML engineers to train, deploy, and monitor models across Recursion's massive datasets. With billions of compounds, 30+ petabytes of experimental data, and complex deep learning workloads, your team enables everything from automated compound screening models to clinical trial prediction systems. You will work closely with researchers and ML engineers to understand their infrastructure needs and build scalable solutions for model development, training, and deployment.\n Act as a mentor, coach, and sponsor - You will share your technical, leadership and managerial skills in MLOps, distributed computing, and infrastructure engineering, delivering impact, learning, and growth across teams at Recursion. We believe that the best work comes from working across organizational boundaries and you will have opportunities to partner with ML research, platform engineering, and business teams.\n Enable a model-driven culture - Machine learning is at the core of everything we do. You will work with stakeholders across the business to ensure our ML infrastructure supports rapid experimentation, reliable model deployment, and continuous improvement. Problems you will work on could range from optimizing GPU cluster utilization to implementing Agentic orchestration and establishing company-wide MLOps standards\n \n The Team You’ll Join: \n You'll be part of a group of technical leaders who work together on the craft of engineering leadership as well as debate ML system architecture, MLOps patterns, and infrastructure optimization strategies. We all work better when we have the support of those around us and are learning together to solve complex problems around model scalability, deployment reliability, and infrastructure efficiency across our teams. You will report to the Executive Director of Engineering who broadly oversees Cloud Infrastructure, High Performance Compute and Machine Learning Infrastructure space.\n The Experience You Will Need: \n \n Experience in a hands-on technical role as a tech lead or a manager with a focus on infrastructure, MLOps and distributed systems. Excitement for deeply engaging in technical details with your team around machine learning, orchestration and agentic systems.\n A people-first mindset. We deliver in a way that prioritizes supporting our coworkers in their growth and experience and understand how Conway's Law shapes our ML system outcomes.\n Demonstrated past record of learning from and teaching peers in areas of ML infrastructure, model deployment, distributed compute, GPU optimization, and MLOps system architecture\n Excitement to learn parts of our ML tech stack that you might not already know. Our current ML infrastructure includes: Python, PyTorch, Docker, Kubernetes, Ray, Weights \u0026 Biases, Prefect, BigQuery, Postgres, GCP, CUDA, and various model serving frameworks.\n Fluency in life sciences or drug discovery is a plus but not required to be considered.\n \n Working Location \u0026 Compensation: \n This is an office-based, hybrid position at our US headquarters located in Salt Lake City, Utah . Employees are expected to work in the office at least 50% of the time.\n At Recursion, we believe that every employee should be compensated fairly. Based on the skill and level of experience required for this role, the estimated current annual base range for this role is $151,130 to $203,490 (USD) . You will also be eligible for an annual bonus and equity compensation, as well as a comprehensive benefits package. \n #LI-EP1\n The Values We Hope You Share: \n \n We act boldly with integrity. We are unconstrained in our thinking, take calculated risks, and push boundaries, but never at the expense of ethics, science, or trust. \n We care deeply and engage directly. Caring means holding a deep sense of responsibility and respect - showing up, speaking honestly, and taking action.\n We learn actively and adapt rapidly. Progress comes from doing. We experiment, test, and refine, embracing iteration over perfection.\n We move with urgency because patients are waiting. Speed isn’t about rushing but about moving the needle every day.\n We take ownership and accountability. Through ownership and acco","salary_min":151130,"salary_max":203490,"location":"Salt Lake City, Utah","workplace":"onsite","job_type":"full-time","experience_level":"lead","tags":["pytorch","deep-learning","cloud","mlops","gpu","llm","distributed-systems","healthcare"],"apply_url":"https://job-boards.greenhouse.io/recursionpharmaceuticals/jobs/7961460","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-29T14:56:13Z","expires_at":"2026-06-29T14:07:04.532978Z","created_at":"2026-05-30T14:07:04.642889Z","updated_at":"2026-05-30T14:07:04.642889Z","company_name":"Recursion","company_slug":"recursion","company_logo_url":"https://www.google.com/s2/favicons?domain=recursion.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/58a5e82c-4cbd-49e1-9ce2-45a7b213b0a9"},{"id":"a944334e-23f0-4033-b1c8-307c9e7c7124","company_id":"75dcf7c0-5121-45f1-8d1b-6bfbfe15072f","title":"Helix AI Engineer, Backend Infrastructure ","slug":"helix-ai-engineer-backend-infrastructure-13269072","description":"Figure is an AI Robotics company developing a general purpose humanoid. Our humanoid robot is designed for commercial tasks and the home. We are based in San Jose and require 5 days/week in-office collaboration. It’s time to build.\n We're looking for a senior-level backend engineer who has scaled high-throughput, low-latency data systems and has strong instincts around cloud infrastructure and real-time streaming pipelines. You'll architect and build the core backend systems that power Figure's real-time data infrastructure — enabling the scale and reliability that our AI and robotics platforms depend on.\n This is a high-ownership role at the intersection of media and sensor data streaming, cloud systems, and applied ML serving. You'll work closely with our AI and robotics teams to ensure latency, reliability, and throughput meet the demands of real-world robot operation.\n WHAT YOU'LL DO \n \n Architect and scale cloud backend infrastructure for high-concurrency, real-time streaming of media and sensor data across robot fleets and user sessions.\n Design and build low-latency data pipelines that ingest, route, and process high-bandwidth streams — including camera feeds, IMU data, and other robot sensor outputs — into our AI stack in real time.\n Own reliability, latency, and throughput SLAs for streaming and data infrastructure.\n Collaborate with AI and robotics teams to integrate ML model serving into real-time data pipelines.\n Build observability, alerting, and tooling to give the team full situational awareness over live robot traffic.\n Drive architectural decisions and mentor engineers across the team.\n \n WHAT WE'RE LOOKING FOR \n \n Deep experience scaling cloud backend systems handling high-concurrency, real-time data streams — media, sensor, telemetry, or equivalent high-bandwidth pipelines.\n Strong fundamentals in distributed systems: stream processing, connection management, data transport, and low-latency architecture.\n Proficiency in one or more backend languages (Go, C++, Python, Rust) and cloud platforms (AWS, GCP, or Azure).\n Experience with containerized infrastructure, service mesh, and large-scale deployment pipelines.\n Strong communication and cross-functional collaboration skills.\n \n NICE TO HAVE \n \n Hands-on experience integrating AI inference serving (Triton Inference Server, TensorRT, SageMaker, or similar) into real-time data pipelines.\n Background in robotics, autonomous vehicles, live media platforms, or other latency-critical streaming domains.\n Familiarity with protocols such as WebRTC, RTSP, gRPC, or Kafka for real-time data transport.\n Experience with on-device or edge inference and the tradeoffs of cloud vs. edge processing.\n \n The US base salary range for this full-time position is between $150,000 - $400,000 annually.\n The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended.","salary_min":150000,"salary_max":400000,"location":"San Jose, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["robotics","mlops","api-design","autonomous-vehicles","cloud","gpu","data-pipeline","distributed-systems"],"apply_url":"https://job-boards.greenhouse.io/figureai/jobs/4685172006","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-28T21:42:25Z","expires_at":"2026-06-29T14:05:53.514412Z","created_at":"2026-05-29T14:18:08.491663Z","updated_at":"2026-05-30T14:05:53.629497Z","company_name":"Figure AI","company_slug":"figure-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=figure.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a944334e-23f0-4033-b1c8-307c9e7c7124"},{"id":"b2263952-2d61-4a59-acd2-4d8506c9b16e","company_id":"cf6855a4-6591-475f-be10-f3f36cf31758","title":"Senior Software Engineer, Search Relevance","slug":"senior-software-engineer-search-relevance-8f221ba2","description":"WHAT IS BOX?  \n Box (NYSE:BOX) is the leader in Intelligent Content Management. Our platform enables organizations to fuel collaboration, manage the entire content lifecycle, secure critical content, and transform business workflows with enterprise AI. We help companies thrive in the new AI-first era of business. Founded in 2005, Box simplifies work for leading global organizations, including JLL, Morgan Stanley, and Nationwide. Box is headquartered in Redwood City, CA, with offices across the United States, Europe, and Asia.\n By joining Box, you will have the unique opportunity to continue driving our platform forward. Content powers how we work. It’s the billions of files and information flowing across teams, departments, and key business processes every single day: contracts, invoices, employee records, financials, product specs, marketing assets, and more. Our mission is to bring intelligence to the world of content management and empower our customers to completely transform workflows across their organizations. With the combination of AI and enterprise content, the opportunity has never been greater to transform how the world works together and at Box you will be on the front lines of this massive shift.\n WHY BOX NEEDS YOU \n The Search Relevance team at Box powers discovery across billions of files, enabling customers to find the right content quickly, securely, and intelligently. As we expand into a new era of AI-powered content understanding, we’re investing in the foundation that makes great search possible: reliable systems, strong signals, and models that learn from real-world usage.\n This is a rare opportunity to work at the intersection of information retrieval science, applied machine learning, and large-scale distributed systems. You’ll be building the infrastructure that powers intelligent content discovery for Fortune 500 companies—where milliseconds matter, relevance is measurable, and your experiments directly impact how millions of users work.\n We’re looking for a Senior Software Engineer to elevate search quality end-to-end—signals, ranking, retrieval, and evaluation—while building scalable, low-latency services that serve queries in real time. You’ll partner with Product, Data, and Infra teams to productionize cutting-edge models and experimentation frameworks, and help define the future of Box’s content intelligence, including hybrid and semantic search and our next-generation content agent.\n WHAT YOU'LL DO  \n \n Build and improve ranking, retrieval, and recommendation systems; identify the right signals and metrics to drive quality improvements that users can feel.\n Apply cutting-edge techniques (embeddings, LLM-enabled retrieval, hybrid search) to productionize experimentation and evaluation pipelines that scale to trillions of documents.\n Define and execute offline/online evaluation, A/B testing, and relevance tuning (NDCG, MRR, precision@k) to continuously improve search outcomes.\n Develop infrastructure for low-latency, high-availability query serving and near real-time indexing across distributed systems.\n Tackle distributed systems challenges including data sharding, intelligent routing, replication, and performance optimization.\n From ETL pipelines and feature engineering to model serving and result ranking—understand how data flows through the system and optimize at every stage.\n Lead design and implementation of new platform components from the ground up; establish patterns, raise the bar on code quality, and champion best practices.\n Share your expertise, contribute to technical direction, conduct thoughtful code reviews, and help shape our engineering culture.\n Participate in our on-call rotation, available at all times while on-call to help respond to and triage any issues that arise.\n \n WHO YOU ARE  \n \n 5+ years of industry experience building and operating backend or distributed systems at scale.\n Strong proficiency in an object-oriented language (e.g., Java, Scala, C++, or Python); Python experience strongly preferred.\n Hands-on experience building ranking, recommendation, NLP, or applied AI platforms in production. You understand the ML lifecycle from training to serving.\n Comfortable with data pipelines, message queues, and/or streaming systems (e.g., Kafka, Pub/Sub) and near real-time data processing.\n Experienced deploying and operating microservices in cloud environments; solid grasp of reliability, observability, and performance best practices.\n BS in Computer Science or related field, or equivalent practical experience.\n AI-first mindset—pragmatic about using the right models, signals, and evaluation methods to improve outcomes quickly and measurably.\n \n PREFERRED \n \n Experience with Elasticsearch, Solr, Lucene, or building custom search systems; deep understanding of inverted indexes, scoring functions, and query optimization.\n Knowledge of ML relevance tuning, learning-to-rank, retrieval evaluation metrics, offline/online testing, and A","salary_min":198500,"salary_max":248000,"location":"Redwood City, CA","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["search","tensorflow","distributed-systems","pytorch","llm","nlp","fine-tuning","mlops"],"apply_url":"https://job-boards.greenhouse.io/boxinc/jobs/7926452","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-28T19:12:52Z","expires_at":"2026-06-29T14:19:20.83221Z","created_at":"2026-05-29T15:11:42.002134Z","updated_at":"2026-05-30T14:19:20.940887Z","company_name":"Box","company_slug":"box","company_logo_url":"https://www.google.com/s2/favicons?domain=box.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/b2263952-2d61-4a59-acd2-4d8506c9b16e"},{"id":"64170ac3-3bc0-4e64-aa55-14d395814525","company_id":"2ca4efa5-edc2-4352-a597-ea27086e1e5b","title":"Senior Machine Learning Engineer II, Ads Response Prediction","slug":"senior-machine-learning-engineer-ii-ads-response-prediction-c8a2de33","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 As a Senior Machine Learning Engineer II on the Ads Response Prediction team, you will lead the design and development of core ML models that power Instacart’s ads ecosystem. This is a research-leaning role focused on theoretical problem formulation, training methodology, and model quality rather than infrastructure or full-stack engineering. You will tackle fundamental challenges in pCTR modeling such as mitigating selection bias, position bias, and optimizer’s curse in training data, improving model calibration across surfaces and domains, and advancing our multi-task learning and sequence modeling capabilities. You will also have the opportunity to shape our next-generation foundation model approach for ads ranking and contribute to cutting-edge retrieval systems like TIGER (Transformer Index for Generative Recommenders), Semantic ID and domain language models.\n The Ads Response Prediction team owns all 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 for next interaction recommendations, LLM integrations, relevance models, pCTR models, bidding models and incrementality models. The team optimizes for an efficient marketplace to ensure delightful customer shopping experience, desirable advertiser business outcome and Instacart Ads revenue.\n The team has strong ML infrastructure and MLOps support, including Delta/DBT-Spark data pipelines, Ray-based distributed training, and automated model deployment. This means you can focus your energy on advancing modeling science rather than building infrastructure.\n About the Job \n \n Lead research and development of pCTR and conversion prediction models, with a focus on improving calibration, reducing training data biases (selection bias, position bias, optimizer’s curse), and advancing model accuracy across Instacart’s ads surfaces.\n Design and implement debiasing techniques such as Mixed Negative Sampling (MNS), Inverse Propensity Weighting (IPW), counterfactual risk minimization, and calibration methods (Platt scaling, isotonic regression) to address systematic prediction biases.\n Contribute to the next-generation Multi-Domain Multi-Task (MDMT) model architecture, incorporating innovations like Mixture-of-Experts (MoE), Transformer layers for sequential user behavior, and LoRA adaptors for scalable domain fine-tuning.\n Drive sequence modeling initiatives including the TIGER generative retrieval system and Semantic ID representation learning, expanding their application across ads surfaces such as Product Details, Search and other placements.\n Collaborate with the broader ML community in the company on the path toward Foundation Models using autoregressive user behavior prediction.\n Formulate and scope ambiguous modeling problems from first principles. Translate business observations (e.g., overcalibration patterns, cold-start underperformance) into well-defined ML research directions with clear evaluation criteria.\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 About You \n Minimum Qualifications \n \n PhD/Master in machine learning, statistics, computer science, information retrieval, or a closely related quantitative field.\n 6+ years of combined academic and industry experience (including PhD research) applying ML to ranking, recommendation, or prediction problems at scale.\n Deep understanding of CTR/conversion prediction modeling, including familiarity with architectures such as Deep \u0026 Wide, DeepFM, DCN, and multi-task learning formulations.\n Strong foundation in causal inference, counterfactual reasoning, and","salary_min":201000,"salary_max":212000,"location":"Remote (US)","workplace":"remote","job_type":"full-time","experience_level":"senior","tags":["llm","pytorch","deep-learning","generative-ai","data-pipeline","mlops","fine-tuning","distributed-systems"],"apply_url":"https://instacart.careers/job/?gh_jid=7963838","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-28T19:10:27Z","expires_at":"2026-06-29T14:08:41.426586Z","created_at":"2026-05-29T14:32:35.186075Z","updated_at":"2026-05-30T14:08:41.541147Z","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/64170ac3-3bc0-4e64-aa55-14d395814525"},{"id":"06abea54-0601-42e3-bb89-32ddb1ee619d","company_id":"3029e985-56bf-4ac2-9ae1-df4cdd53b12f","title":"Sr. Staff Software Development Engineer-AI Security","slug":"sr-staff-software-development-engineer-ai-security-6fd417d1","description":"About Zscaler \n Zscaler accelerates digital transformation to ensure our customers can be more agile, efficient, resilient, and secure. As an AI-forward enterprise , we are constantly pushing the envelope, leveraging the world’s largest security data lake to power our cloud-native Zero Trust Exchange platform. This innovation protects our customers from cyberattacks and data loss by securely connecting users, devices, and applications in any location.\n Here, impact in your role matters more than title and trust is built on results. We say, impact over activity. We seek innovators who actively use AI to amplify their impact and who thrive in an environment where we leverage intelligent systems to stay ahead of evolving threats. We believe in transparency and value constructive, honest debate —we’re focused on getting to the best ideas, faster. We build high-performing teams that can make an impact quickly and with high quality. To do this, we are building a culture of execution centered on customer obsession , collaboration, ownership, and accountability.\n We value high-impact, high-accountability with a sense of urgency where you’re enabled to do your best work and embrace your potential. If you’re driven by purpose, thrive on solving complex challenges, and want to be part of the team that’s helping to secure the AI age, we invite you to bring your talents to Zscaler and help shape the future of cybersecurity.\n Role \n We are looking for a Sr. Staff Software Development Engineer-AI Security to join us as a founding member of our AI Security Team. This is a Hybrid role based in San Jose, CA or Bellevue, WA (3 days in office), reporting to the Director of Software Engineering within the Emerging Tech org.\n You will be responsible for designing and implementing core infrastructure components and distributed systems, serving as a foundational architect for our AI security solution. This high-impact role focuses on scaling security infrastructure to support hundreds of millions of users, collaborating with stakeholders across the development lifecycle to drive innovation and technical excellence.\n What you’ll do (Role Expectations) \n \n Architect, develop, and optimize a low-latency, high-throughput AI Security plane utilizing Rust, specifically leveraging its async/await model for highly efficient I/O and service-oriented architecture\n Build resilient, distributed, and scalable systems, emphasizing concurrency, fault tolerance, and robust messaging protocols\n Implement and maintain gRPC services and APIs to ensure seamless integration of the AI Security plane with control and orchestration infrastructure\n Systematically enhance performance across the entire stack, including LLM models, by employing profiling tools for both kernel-space and user-space components\n Lead complex, multi-functional projects and initiatives, defining the technical roadmap and driving execution across teams\n \n Who You Are (Success Profile) \n \n You thrive in ambiguity. You're comfortable building the path as you walk it. You thrive in a dynamic environment, seeing ambiguity not as a hindrance, but as the raw material to build something meaningful.\n You act like an owner. Your passion for the mission fuels your bias for action. You operate with integrity because you genuinely care about the outcome. True ownership involves leveraging dynamic range: the ability to navigate seamlessly between high-level strategy and hands-on execution.\n You are a problem-solver. You love running towards the challenges because you are laser-focused on finding the solution, knowing that solving the hard problems delivers the biggest impact.\n You are a high-trust collaborator. You are ambitious for the team, not just yourself. You embrace our challenge culture by giving and receiving ongoing feedback—knowing that candor delivered with clarity and respect is the truest form of teamwork and the fastest way to earn trust.\n You are a learner. You have a true growth mindset and are obsessed with your own development, actively seeking feedback to become a better partner and a stronger teammate. You love what you do and you do it with purpose.\n \n What We’re Looking for (Minimum Qualifications) \n \n 8+ years of software engineering experience\n Deep experience in systems programming using Rust, with a focus on asynchronous frameworks such as Tokio or async-std\n Proven ability to design and implement horizontally scalable, highly available, and observable distributed systems\n Strong command of Linux internals, including kernel-user space interaction, networking, sockets, and namespaces\n Skilled in performance instrumentation, containerized environments, Git workflows, and CI/CD pipelines\n \n What Will Make You Stand Out (Preferred Qualifications) \n \n Expert in systems languages such as C/C++ or Rust, with a focus on performance optimization\n Deep understanding of Linux networking stacks, Kubernetes networking, service meshes, and LLM model optimization\n ","salary_min":154000,"salary_max":220000,"location":"Bellevue, WA","workplace":"hybrid","job_type":"full-time","experience_level":"lead","tags":["security","distributed-systems","api-design","llm","data-pipeline","agents"],"apply_url":"https://job-boards.greenhouse.io/zscaler/jobs/5146138007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-28T15:07:34Z","expires_at":"2026-06-29T14:09:19.468531Z","created_at":"2026-05-29T14:33:11.60717Z","updated_at":"2026-05-30T14:09:19.582996Z","company_name":"Zscaler","company_slug":"zscaler","company_logo_url":"https://www.google.com/s2/favicons?domain=zscaler.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/06abea54-0601-42e3-bb89-32ddb1ee619d"},{"id":"ceb7845a-f491-495f-b9ad-afc4cbf8eff5","company_id":"3029e985-56bf-4ac2-9ae1-df4cdd53b12f","title":"Sr. Software Development Engineer-AI Security","slug":"sr-software-development-engineer-ai-security-12d276fe","description":"About Zscaler \n Zscaler accelerates digital transformation to ensure our customers can be more agile, efficient, resilient, and secure. As an AI-forward enterprise , we are constantly pushing the envelope, leveraging the world’s largest security data lake to power our cloud-native Zero Trust Exchange platform. This innovation protects our customers from cyberattacks and data loss by securely connecting users, devices, and applications in any location.\n Here, impact in your role matters more than title and trust is built on results. We say, impact over activity. We seek innovators who actively use AI to amplify their impact and who thrive in an environment where we leverage intelligent systems to stay ahead of evolving threats. We believe in transparency and value constructive, honest debate —we’re focused on getting to the best ideas, faster. We build high-performing teams that can make an impact quickly and with high quality. To do this, we are building a culture of execution centered on customer obsession , collaboration, ownership, and accountability.\n We value high-impact, high-accountability with a sense of urgency where you’re enabled to do your best work and embrace your potential. If you’re driven by purpose, thrive on solving complex challenges, and want to be part of the team that’s helping to secure the AI age, we invite you to bring your talents to Zscaler and help shape the future of cybersecurity.\n Role \n We are looking for a Senior Software Development Engineer-AI Security to join us as a founding member of our AI Security Team. This is a Hybrid role based in San Jose, CA or Bellevue, WA (3 days in office), reporting to the Director of Software Engineering within the Emerging Tech org.\n You will build a high-reliability, low-latency AI security solution capable of scaling to hundreds of millions of users. In this role, you will be crucial in enhancing security capabilities for the AI within the world's largest cloud security platform by designing and implementing core infrastructure components and distributed systems while collaborating closely with stakeholders throughout the development lifecycle.\n What you’ll do (Role Expectations) \n \n Develop high-performance networking code for multiple desktop platforms using the Rust language and platform-native APIs\n Improve code quality through building solid, testable, and well-documented software foundations\n Design and implement major development projects with a focus on scalability, security, and performance\n Collaborate with product managers and cross-functional teams to deliver customer-impacting features\n Debug and solve complex network-related problems and enhance system functionality\n \n Who You Are (Success Profile) \n \n You thrive in ambiguity. You're comfortable building the path as you walk it. You thrive in a dynamic environment, seeing ambiguity not as a hindrance, but as the raw material to build something meaningful.\n You act like an owner. Your passion for the mission fuels your bias for action. You operate with integrity because you genuinely care about the outcome. True ownership involves leveraging dynamic range: the ability to navigate seamlessly between high-level strategy and hands-on execution.\n You are a problem-solver. You love running towards the challenges because you are laser-focused on finding the solution, knowing that solving the hard problems delivers the biggest impact.\n You are a high-trust collaborator. You are ambitious for the team, not just yourself. You embrace our challenge culture by giving and receiving ongoing feedback—knowing that candor delivered with clarity and respect is the truest form of teamwork and the fastest way to earn trust.\n You are a learner. You have a true growth mindset and are obsessed with your own development, actively seeking feedback to become a better partner and a stronger teammate. You love what you do and you do it with purpose.\n \n What We’re Looking for (Minimum Qualifications) \n \n Bachelor’s degree in computer science, engineering, or a related field\n 3+ years of software engineering experience with deep expertise in the Rust programming language and familiarity with lower-level languages such as C/C++\n Strong knowledge of system and network programming including firewalls, VPNs, protocols, TCP/IP, UDP, DNS, QUIC, H/3, and proxies\n Familiarity with system concepts such as virtual memory, multi-threading, and system APIs, and familiarity with SLM and LLM models\n Excellent debugging and problem-solving skills in both networking and system-level contexts\n \n What Will Make You Stand Out (Preferred Qualifications) \n \n Familiarity with DevOps pipelines, VPN technologies, and a strong understanding of security protocols and standards\n Experience writing testable, low-complexity code with dependency injection and thorough documentation\n Proficiency in additional programming languages like Swift, Python, or comparable technologies; direct experience in validating AI-d","salary_min":112000,"salary_max":160000,"location":"Bellevue, WA","workplace":"hybrid","job_type":"full-time","experience_level":"senior","tags":["mlops","data-pipeline","agents","security","llm","distributed-systems"],"apply_url":"https://job-boards.greenhouse.io/zscaler/jobs/5146134007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-28T14:55:45Z","expires_at":"2026-06-29T14:09:19.312884Z","created_at":"2026-05-29T14:33:11.44252Z","updated_at":"2026-05-30T14:09:19.425288Z","company_name":"Zscaler","company_slug":"zscaler","company_logo_url":"https://www.google.com/s2/favicons?domain=zscaler.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/ceb7845a-f491-495f-b9ad-afc4cbf8eff5"},{"id":"f18a9045-c453-4617-84c9-63c285e45fbc","company_id":"65bce624-56a0-4c48-8e69-eda588090849","title":"Full Stack Engineer, AgentControl","slug":"full-stack-engineer-agentcontrol-9ca8eb11","description":"About the Job: \n As a Full Stack Engineer on LaunchDarkly’s AgentControl team, you'll build critical systems such as the web application that customers interact with on a daily basis and distributed systems that power AI evaluations at scale. These systems enable customers to control, monitor, and optimize their agent’s functionality. You’ll primarily work in Go, Python, and Typescript and will work directly with other technologies and tools such as AWS, CockroachDB, and Datadog.\n LaunchDarkly’s AgentControl team is on a mission to manage the complete software development lifecycle for shipping agents to production, from configuring to benchmarking to observing and beyond. This team has a huge opportunity ahead of it; we're growing fast and we need your help writing the next chapter in our story.\n Responsibilities:\n \n \n Build and maintain scalable backend services and APIs that power the AI Configs product\n \n Collaborate with internal stakeholders, including product managers, designers, and other LaunchDarkly engineering teams, to understand real-world challenges and improve GenAI workflows using our own platform.\n \n Continuously optimize the performance, reliability, and scalability of our systems\n \n Participate in architecture and design discussions, offering thoughtful input and collaborating with senior engineers to identify tradeoffs and make well-informed decisions\n \n Keep up-to-date with the latest developments in the AI field and in software development best practices\n \n Take ownership of code in production, participate in on-call rotations, and help improve the reliability and maintainability of our systems over time.\n \n Qualifications:\n \n \n 5+ years of professional software engineering experience, with a track record of shipping production-quality full-stack features.\n \n Hands-on experience building GenAI features, Agents, and/or working with Large Language Models (LLMs), either through direct model integration or leveraging AI APIs like OpenAI, Google Cloud AI, or AWS Bedrock.\n \n Understanding of prompt engineering, fine-tuning models, or deploying AI services in production environments.\n \n Experience with designing, implementing, and maintaining RESTful APIs\n \n Experience writing production-ready code with emphasis on quality and maintainability\n \n Experience with distributed systems or data ingestion\n \n Strong computer science fundamentals\n \n Committed to working in a communicative, collaborative environment\n \n Comfortable navigating ambiguous challenges in a rapidly evolving domain, and excited about staying at the forefront of AI advancements\n \n Strong sense of ownership and accountability for delivering impactful solutions\n \n Proven ability to work closely with Product and Design teams to define requirements for new features in a fast-paced iterative environment\n \n \n Pay: \n Target pay ranges based on Geographic Zones* for Level 3: \n \n Zone 1: San Francisco/Bay Area or NYC Metropolitan Area, Boston, Seattle - $171,200 - $235,400**\n Zone 2: Irvine, LA, Monterey, Santa Barbara, Santa Rosa, Austin, Portland, Philadelphia, Chicago - $154,100 - $211,860**\n Zone 3: All other US locations - $145,500 - $200,090**\n \n LaunchDarkly operates from a place of high trust and transparency; we are happy to state the pay range for our open roles to best align with your needs. Exact compensation may vary based on skills, experience, and location. \n *Within the United States, our geographic pay zones are defined by counties surrounding major metropolitan areas. **Restricted Stock Units (RSUs), health, vision, and dental insurance, and mental health benefits in addition to salary. \n About LaunchDarkly: \n Modern software delivery was supposed to be the foundation for a thriving digital business but reality has proven otherwise. Slow, inefficient development cycles, costly outages, and fragmented customer experiences are preventing developers from building their best software. The LaunchDarkly platform helps developers innovate on new features faster while protecting them with a safety valve to instantly rewind when things go wrong. Developers can target product experiences to any customer segment and maximize the business impact of every feature. And by gradually rolling out new application components, they escape nightmare \"big-bang\" technology migrations. \n The LaunchDarkly platform was built to guide engineers to the next frontier of DevOps by:\n \n Improving the velocity and stability of software releases, without the fear of end customer outages\n Delivering targeted experiences by easily personalizing features to customer cohorts\n Maximizing the business impact of every feature through the ability to experiment and optimize\n Coordinating the release and optimization of software to provide consistent experiences across mobile platforms and device types\n Improving the effectiveness and productivity of engineering teams, by providing insights into engineering cadence and stability\n \n At LaunchDarkly, we b","salary_min":145500,"salary_max":200090,"location":"Remote (US)","workplace":"remote","job_type":"full-time","experience_level":"senior","tags":["distributed-systems","llm","fine-tuning","generative-ai","cloud","fullstack"],"apply_url":"https://job-boards.greenhouse.io/launchdarkly/jobs/7750116003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-27T22:02:02Z","expires_at":"2026-06-29T14:16:49.153499Z","created_at":"2026-05-28T14:18:25.498364Z","updated_at":"2026-05-30T14:16:49.263304Z","company_name":"LaunchDarkly","company_slug":"launchdarkly","company_logo_url":"https://www.google.com/s2/favicons?domain=launchdarkly.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f18a9045-c453-4617-84c9-63c285e45fbc"},{"id":"2f82717a-ca5c-44ec-afbc-871db9888784","company_id":"f36ec848-cb19-4b95-a680-6733e58086c0","title":"Director, Data Science","slug":"director-data-science-e0c2bfe0","description":"May Mobility is transforming cities through autonomous technology to create a safer, greener, more accessible world. Based in Ann Arbor, Michigan, May develops and deploys autonomous vehicles (AVs) powered by our innovative Multi-Policy Decision Making (MPDM) technology that literally reimagines the way AVs think. Our vehicles do more than just drive themselves - they provide value to communities, bridge public transit gaps and move people where they need to go safely, easily and with a lot more fun. We’re building the world’s best autonomy system to reimagine transit by minimizing congestion, expanding access and encouraging better land use in order to foster more green, vibrant and livable spaces. Since our founding in 2017, we’ve given more than 500,000 autonomous rides to real people around the globe. And we’re just getting started. We’re hiring people who share our passion for building the future, today, solving real-world problems and seeing the impact of their work. Join us. \n Job Summary \n May Mobility is entering an exciting phase of growth as we expand our autonomous transit and mobility services across the country. Founded in 2017 by a team of experienced roboticists, perception, behavior, AI, and software engineers, we operate driverless transit shuttles in real communities — not as a research demonstration, but as a daily-service product that people rely on to get to work, school, and home.\n The Director, Data Science will lead the team responsible for turning the data generated by our fleet, simulation environment, and ML systems into the insights, evaluations, and decisions that make our autonomous service safer, more efficient, and ready to scale into new cities. You will own data science across simulation and synthetic data, perception and planning ML evaluation, fleet operations analytics, and the data infrastructure that supports them. You will partner directly with Engineering, Product, Operations, and Safety leadership to set measurement standards, define release criteria, and translate frontline operating data into the next generation of our autonomy stack.\n This is a leadership role for someone who has scaled a data science function inside a hard-tech environment, who is comfortable making engineering and product tradeoffs alongside their team, and who sees the gap between research-grade ML and production transit-grade ML as the most interesting problem in the industry today.\n Essential Responsibilities \n \n Set and own the data science strategy across simulation and synthetic data, ML evaluation (perception, prediction, planning), fleet operations analytics, and the data platform that supports them; translate that strategy into a 12–24 month roadmap with measurable milestones.\n Lead, grow, and develop a team of senior data scientists, ML engineers, and front-line managers; recruit from a small expert pool, calibrate the bar, and build a hiring brand that allows May Mobility to win against AV, robotics, and AI competitors.\n Partner with Engineering, Product, Safety, and Operations leaders to define release criteria, performance metrics, and ODD-expansion gates; use data to make the business case for what we deploy, where, and when.\n Drive ML and analytics applications end-to-end: dataset curation, scenario coverage, modeling, offboard evaluation, productionization, and continuous monitoring of fleet performance in the wild.\n Establish measurement and experimentation standards across the company — including before/after analyses for stack changes, A/B-style comparisons in simulation, and statistically credible reporting on real-world incidents.\n Lead team-wide quality activities including design and code reviews; hold the bar on engineering rigor for production data science systems.\n Track and trend technical performance of the autonomy stack in the field; surface root causes, prioritize fixes with engineering, and represent fleet-data findings to executives, regulators, and partners.\n Provide technical guidance to Engineering and Operations leaders on issue diagnosis, resolution, and the ML changes most likely to move our key safety and service metrics.\n Represent May Mobility's data science work externally where appropriate — through publications, conference talks, partner reviews, and recruiting.\n \n Skills and Abilities \n Success in this role typically requires the following competencies: \n \n Autonomy Data Expertise. Can reason fluently about the data produced by a modern AV stack — sensor logs, perception outputs, planning traces, simulator results, and operational telemetry — and can identify which signals matter for which decisions.\n Hands-On Technical Depth. Has personally shipped production ML or analytics systems within the last 3–5 years and is credible in code review and design review with senior engineers and scientists.\n Cross-Functional Translator. Can explain a complex ML or statistical finding to engineering, product, and executive audiences; and ","salary_min":217000,"salary_max":312000,"location":"Remote (US)","workplace":"remote","job_type":"full-time","experience_level":"lead","tags":["robotics","healthcare","distributed-systems","pytorch","computer-vision","tensorflow","reinforcement-learning","autonomous-vehicles"],"apply_url":"https://job-boards.greenhouse.io/maymobility/jobs/8561428002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-27T21:54:47Z","expires_at":"2026-06-29T14:17:06.3175Z","created_at":"2026-05-28T14:18:43.046233Z","updated_at":"2026-05-30T14:17:06.431533Z","company_name":"May Mobility","company_slug":"may-mobility","company_logo_url":"https://www.google.com/s2/favicons?domain=maymobility.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/2f82717a-ca5c-44ec-afbc-871db9888784"},{"id":"fde598de-15bc-4dbe-bde8-a8635779a8bd","company_id":"776e5e7d-beba-4889-a481-6d9d7c3af325","title":"Engineering Manager, Agents","slug":"engineering-manager-agents-ba9a26b5","description":"About Decagon\n\nDecagon is the leading conversational AI platform empowering every brand to deliver concierge customer experiences.\n\nOur technology enables industry-defining enterprises like Avis Budget Group, Block’s Cash App and Square, Chime, Oura Health, and Hunter Douglas to deploy AI agents that power personalized, deeply satisfying interactions across voice, chat, email, SMS, and every other channel.\n\nWe’re building a future where customer experiences are being redefined from support tickets and hold music to faster resolutions, richer conversations, and deeper relationships. We’re proud to be backed by world-class investors who share that vision, including a16z, Accel, Bain Capital Ventures, Coatue, and Index Ventures, along with many others.\n\nWe’re an in-office company, driven by a shared commitment to excellence and velocity. Our values — Just Get It Done, Invent What Customers Want, Winner’s Mindset, and The Polymath Principle — shape how we work and grow as a team.\n\n\n\nAbout the Team\n\nThe Agent Engineering team at Decagon deploys mission-critical AI agents to our customers that impact millions of users and directly drive Decagon’s growth. You will lead a team building on our industry-leading AI agent platform, collaborate directly with customers and help devise long-term, scalable solutions.\n\nOur mission is to deliver magical support experiences — AI agents working alongside human agents to help users resolve their issues.\n\n\nAbout the Role\n\nAs an Engineering Manager on the Agent Engineering team, you’ll lead a group of engineers building and shipping best-in-class AI agents, from initial implementation through continuous iteration. You’ll work directly with leaders across industries like finance, healthcare and hospitality, solving their users’ needs with reliable and intuitive AI agents.\n\nManagers here are expected to operate with high ownership and technical depth while helping their teams move quickly and maintain a high quality bar. This role is for someone who enjoys mentoring engineers, partnering closely with customers and diving deep into complex system challenges to build elegant solutions that scale to millions of users.\n\n\nIn this role, you will\n\n - Lead and grow a team of engineers building AI agents that outperform human agents in managing complex customer interactions and driving customer retention\n\n - Partner directly with enterprise customers to understand their operational pain points and translate them into scalable AI agent solutions\n\n - Drive execution across the full lifecycle of agent deployments, from initial implementation through continuous iteration and optimization\n\n - Partner with product, design and research to identify cross-customer trends that guide the evolution of Decagon’s agent platform and research efforts\n\n - Help define the technical strategy and roadmap for the future of AI-powered customer support\n\n - Support and mentor engineers through technical guidance, feedback and career development\n\n - Maintain a high engineering bar while fostering a culture of ownership, velocity and customer obsession\n   \n\nYour background looks something like this\n\n - Have 1+ years of engineering management experience\n\n - Have 5+ years of industry experience in software engineering\n\n - Proficiency with Python, Typescript and asynchronous programming\n\n - Experience leading teams building complex distributed systems or customer-facing products\n\n - A high degree of comfort digging into system failures within deep technology stacks using any tool necessary\n\n - Strong communication skills and ability to work directly with enterprise customers\n   \n\nEven better\n\n - Prior experience working with multi-modal models\n\n - Experience leading teams working on AI systems, LLM applications or agentic workflows\n\n\n\nCompensation\n\n$200K – $400K + Offers Equity\n\nThis range reflects the expected compensation for this role. Compensation within the range is determined based on experience, skills, and the scope of responsibilities, with flexibility for candidates who demonstrate exceptional impact.\n\nIn addition to base salary, we offer competitive equity. Final compensation may vary based on location within the United States.\n\n\n\nBenefits\n\nWe proudly offer the following benefits for our full-time employees:\n\n - Take what you need vacation policy (subject to local requirements; UK employees receive 25 days of statutory leave)\n\n - Medical, Dental, and Vision benefits for you and your family\n\n - Life Insurance and Disability Benefits\n\n - Retirement Plan (e.g., 401K, pension)\n\n - Parental Leave\n\n - Fertility and family building benefits through Carrot\n\n - Daily lunches and snacks in the office to keep you at your best\n\nThese benefits are described in more detail in Decagon’s policies, may vary by location, and can change at any time according to applicable compensation and benefits plans.","salary_min":200000,"salary_max":400000,"location":"New York, NY","workplace":"onsite","job_type":"full-time","experience_level":"senior","tags":["agents","distributed-systems","llm","healthcare"],"apply_url":"https://jobs.ashbyhq.com/decagon/0902f176-33a3-4233-be8a-1e22d1e8d23d/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-27T18:38:19.97Z","expires_at":"2026-06-29T14:07:13.313017Z","created_at":"2026-05-28T14:08:44.791187Z","updated_at":"2026-05-30T14:07:13.432945Z","company_name":"Decagon","company_slug":"decagon","company_logo_url":"https://www.google.com/s2/favicons?domain=decagon.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/fde598de-15bc-4dbe-bde8-a8635779a8bd"},{"id":"0ffaba88-636c-40f6-b308-69d1b07a2471","company_id":"238f4b8f-1e78-4053-9068-017564d76785","title":"Senior Platform AI Engineer","slug":"senior-platform-ai-engineer-4ad89166","description":"Our Mission \u0026 Values:\nAt Drata, we help companies earn and keep the trust of their users, customers, partners, and prospects. We’re the proof layer that shows great companies deserve the trust they aim to build.\n\nWe live our values every day. Built on Trust means consistency is everything. Act with Integrity by always doing the right thing. Being Customer-Obsessed keeps the people we serve at the center of our work. Competitive Fire drives us to push ourselves harder than anyone else. Diversity brings unique perspectives that lead to better solutions. Automation First ensures we save time and money by making efficiency a priority.\n\nOur Culture \u0026 Work Style 🚀\n\nAt Drata, we’re not just building software - we’re building a mindset. Everything we do springs from:\n\n - Be a Driver (Owner‑Operator Mentality): Own your work. Improve relentlessly. Deliver results.\n\n - Move at Drata Speed (Precision \u0026 Velocity): Fast decisions. Quick learning. Immediate impact.\n\n - Stay Mission-Driven (Customer‑Obsessed): Challenge assumptions. Deliver value. Stay hungry.\n\nWe pair that high-velocity culture with a thoughtful hybrid model because we believe flexibility and collaboration both matter. That’s why in the Bay we come together in-office Tuesday through Thursday our high‑impact collaboration days where teams align, strategize, and innovate. Mondays and Fridays are flexible, giving you space for focused work, balance, and autonomy.\n\nIf you thrive when you’re empowered, energized, and working with smart, mission-driven people, you’ll feel at home here.\n\nWhy Join The Drata Team?\n\nThe best way to understand the Driver’s Mindset is to see it in action. We’re an award-winning, mission-driven team of 600+ people worldwide, united by a culture that values trust, speed, and continuous growth.\n\n - See the Speed: https://www.youtube.com/watch?v=QidTdkGwKMY Watch our CEO, Adam Markowitz, discuss the hyper-growth journey, from $0 to $100M ARR in just four years\n\n - Hear the Voice of the Team https://drata.com/about/life-at-drata: Explore our \"Life at Drata\" page for employee testimonials on our collaborative and the growth opportunities available.\n\n - Experience the Impact https://www.greatplacetowork.com/certified-company/7044563: See why we are consistently recognized on Fortune's Best Workplaces lists.\n\n - Connect with Us on Socials: LinkedIn https://www.linkedin.com/company/drata/posts/?feedView=all - follow us for company updates, employee stories, and career news.\n\nJob Summary:\n\nDrata's AI Platform team builds the production infrastructure that powers AI features across our compliance platform — from MCP servers that make Drata's data available to AI agents, to LLM workflow orchestration that automates SOC 2, TPRM, and policy analysis. You'll own the systems that sit between our AI models and our customers: tool definitions that agents actually understand, deployment pipelines that handle model upgrades without breaking output quality, and orchestration layers that manage multi-step agent workflows with persistent state.\n\nThis is not a traditional infrastructure role. You'll debug prompt templates alongside Terraform modules. You'll design API schemas optimized for LLM token budgets, not just HTTP throughput. When a model upgrade changes behavior across 15 workflows, you'll assess quality impact — not just confirm the containers are healthy.\n\nYou'll work closely with our agent developers, product engineers, and an embedded SRE partner, sitting at the intersection of AI development and production reliability.\n\nOur north star is simple: minimize the time it takes to launch a new agent in production. You're someone who asks \"are we solving the right problem?\" before writing the first line of code, who builds systems that make five other engineers faster, not just yourself, and who's equally proud of what they chose not to build.\n\nWhat you'll do:\n\n\nMCP SERVER DEVELOPMENT \u0026 AI-OPTIMIZED API DESIGN\n\n - Design and build MCP (Model Context Protocol) servers that expose Drata's platform to AI agents. This means making architectural decisions about tool granularity, naming conventions for agent disambiguation, response compression for LLM context windows, and workspace isolation for multi-tenant access. You'll own the protocol layer that determines whether agents can reliably find and use the right tools — writing semantic parameter descriptions, contextual hints, and tool schemas that optimize for model comprehension, not just developer ergonomics.\n\n\nAGENT ORCHESTRATION \u0026 WORKFLOW INFRASTRUCTURE\n\n - Build and operate the infrastructure for deploying multi-step agent workflows — state management across complex reasoning chains, tool routing and execution runtimes, and long-running agentic processes that persist over time. Own the orchestration layer that coordinates agent planning, tool calls, and human-in-the-loop patterns. Design systems that handle agent failure modes gracefully: retries on ambiguous tool ","salary_min":192000,"salary_max":259800,"location":"San Francisco, CA","workplace":"hybrid","job_type":"full-time","experience_level":"senior","tags":["distributed-systems","search","healthcare","llm","cloud","rag","agents","api-design"],"apply_url":"https://jobs.ashbyhq.com/drata/f0ab62fb-c0a8-4bf2-bfd6-9d9d2e68fb91/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-26T23:02:26.469Z","expires_at":"2026-06-29T14:13:56.372048Z","created_at":"2026-05-27T14:14:32.001255Z","updated_at":"2026-05-30T14:13:56.493566Z","company_name":"Drata","company_slug":"drata","company_logo_url":"https://www.google.com/s2/favicons?domain=drata.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/0ffaba88-636c-40f6-b308-69d1b07a2471"},{"id":"1d143344-1df1-4f1b-9d3d-471e99c3eb21","company_id":"baca6349-80b0-417a-97a1-b31511860322","title":"Site Reliability Engineer","slug":"site-reliability-engineer-0d3e749c","description":"Runpod is the foundational platform for developers to build and run custom AI systems that scale. With over 500,000 developers worldwide and an annual recurring revenue run rate exceeding $120M, Runpod operates at the intersection of developer velocity and production-scale AI. Founded in 2022, we’ve grown rapidly by building infrastructure purpose-built for modern AI workloads. Our platform enables teams to move from experimentation to deployment with flexibility across cloud, on-prem, and hybrid environments. As a remote-first, globally distributed company, we are building the infrastructure layer that powers the next generation of AI systems.\n The Reliability team owns the availability, performance, and operational excellence of Runpod’s global platform. While infrastructure teams build the systems, the Reliability team ensures those systems remain resilient, observable, and scalable under real-world production conditions.\n This team is responsible for:\n \n Defining and enforcing reliability standards across engineering\n Designing incident response processes and improving recovery times\n Building observability systems and reliability tooling\n Driving SLO adoption and production readiness reviews\n \n Reducing operational toil through automation\n The Reliability team works cross-functionally with Infrastructure, Product Engineering, and Support to ensure our systems remain stable and performant as we scale rapidly. We value proactive problem solving, automation-first thinking, and strong ownership of production systems.\n As a Site Reliability Engineer on the Reliability team, you will focus on ensuring the stability and resilience of Runpod’s distributed platform. You will partner with engineering teams to improve system design, strengthen observability, and prevent incidents before they happen.\n This role blends software engineering with production operations. You’ll work on reliability frameworks, SLO design, automation, and production hardening, reducing errors and improving performance across different services and infrastructure.\n This is a high-impact role central to maintaining trust with developers running critical AI workloads on Runpod.\n Your Impact \n \n Increase platform uptime and reduce incident frequency and duration\n Establish and operationalize SLIs/SLOs across services\n Improve MTTR through better tooling, automation, and runbooks\n Strengthen production readiness standards\n Drive long-term systemic reliability improvements\n \n You will influence how reliability is defined and measured across Runpod and help build the operational backbone of the company.\n Responsibilities: \n Reliability Engineering \n \n Define and implement SLIs/SLOs for critical services\n Lead incident response and coordinate cross-team mitigation efforts\n Conduct blameless postmortems and ensure corrective actions are completed\n Perform production readiness reviews for new services and features\n Identify systemic risks and drive preventative improvements\n \n Observability \u0026 Monitoring \n \n Design and improve monitoring, alerting, and dashboards (Prometheus, Grafana, etc.)\n Improve signal-to-noise ratio in alerts and reduce alert fatigue\n Build internal tooling for reliability tracking and reporting\n Improve visibility into GPU performance and distributed systems health\n \n Automation \u0026 Toil Reduction \n \n Automate recurring operational workflows\n Build tools and scripts (Python, Go, Bash) to eliminate manual processes\n Improve deployment safety through automation and guardrails\n Strengthen CI/CD reliability and release processes\n \n Cross-Functional Reliability Advocacy \n \n Partner with engineering teams to improve system resilience\n Provide guidance on fault tolerance, scalability, and failure handling\n Contribute to architectural discussions with a reliability-first mindset\n \n Requirements: \n \n 5+ years of experience in SRE, Reliability Engineering, or Production Engineering\n Strong Linux systems and Networking expertise\n Experience managing containerized production systems\n Strong understanding of distributed systems and failure modes\n Experience defining and managing SLIs/SLOs\n Proven incident response and postmortem leadership experience\n Strong scripting or programming skills\n Experience with monitoring and alerting systems\n Excellent written communication skills\n Successful completion of a background check\n \n Preferred: \n \n Experience with GPU infrastructure or AI/ML platforms\n Experience improving reliability in high-growth or large scale environments\n Familiarity with GPU observability tooling\n Experience with Infrastructure as Code\n Experience working in startup environments\n Experience building internal reliability platforms or frameworks\n \n What You’ll Receive: \n \n The competitive base pay for this position ranges from $150,000- $200,000 usd. This salary range may be inclusive of several career levels at Runpod and will be narrowed during the interview process based on a number of factors, including the candida","salary_min":150000,"salary_max":200000,"location":"Remote (US)","workplace":"hybrid","job_type":"full-time","experience_level":"senior","tags":["gpu","distributed-systems","infrastructure","devops"],"apply_url":"https://job-boards.greenhouse.io/runpod/jobs/5229443008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-22T19:03:55Z","expires_at":"2026-06-29T14:13:40.120853Z","created_at":"2026-05-27T14:14:13.643293Z","updated_at":"2026-05-30T14:13:40.241741Z","company_name":"RunPod","company_slug":"runpod","company_logo_url":"https://www.google.com/s2/favicons?domain=runpod.io\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/1d143344-1df1-4f1b-9d3d-471e99c3eb21"},{"id":"de07a07f-bf9b-404c-afb9-f5c999826ed8","company_id":"5d6de1f6-4d6c-463b-8a2b-a5caeadb97b4","title":"Software Engineer - Airflow Infrastructure","slug":"software-engineer-airflow-infrastructure-27365068","description":"Astronomer empowers data teams to bring mission-critical software, analytics, and AI to life and is the company behind Astro, the industry-leading unified DataOps platform powered by Apache Airflow®. Astro accelerates building reliable data products that unlock insights, unleash AI value, and powers data-driven applications. Trusted by more than 800 of the world's leading enterprises, Astronomer lets businesses do more with their data. To learn more, visit www.astronomer.io http://www.astronomer.io.\n\n\nABOUT THIS ROLE:\n\nAt Astronomer, we’re redefining how companies run Apache Airflow at scale. Our R\u0026D organization is home to some of the most innovative minds in cloud infrastructure and open-source software.\n\nWe’re looking for a Software Engineer to join our Airflow Infra team, part of Astro, our flagship cloud platform. You’ll be building the critical layer that connects the open-source Airflow ecosystem to enterprise-grade, massively scalable cloud infrastructure.\n\nYour work will directly influence how global organizations orchestrate data pipelines at scale - making them faster, more reliable, and easier to manage.\n\nIf you’re driven by impact, excited by scale, and ready to work on the kind of infrastructure challenges that push the boundaries of what’s possible in cloud-native systems, this is the opportunity you’ve been waiting for.\n\n\n\nThis role is based in New York City with a hybrid work schedule.\n\n\n\n\nWHAT YOU GET TO DO:\n\n - Engineer backend services with high quality, maintainable and well tested code.\n\n - Partner with other engineers, product, customer reliability support, and leadership to achieve business goals and define how our systems should evolve.\n\n - Regularly engage in code reviews and provide constructive feedback.\n\n - Optimize the performance, reliability and scalability of existing backend services.\n\n - Investigate, prototype and propose ideas to improve user experience.\n\n - Create and maintain technical documentation for systems and processes, ensuring clarity and accessibility.\n\n - Participate in on-call rotation, troubleshoot and debug to solve incidents.\n\n\n\n\nWHAT YOU BRING TO THE ROLE:\n\n - 3-5 years of experience in Python / Golang and Kubernetes.\n\n - Solid understanding of and experience with integrating with RESTful APIs and distributed systems.\n\n - Comfortable with testing frameworks, such as pytest.\n\n - Strong communication skills, both written and verbal, with experience in creating technical specifications.\n\n - A passion for reliability and operational excellence.\n\n - Ability to scope work and coordinate cross-functionally to address risks and ensure successful delivery.\n\n - Experience with software development best practices, such as code reviews, testing, CI/CD, version control, automation and debugging.\n\n - Ability to adjust to change and rapid pace of development.\n\n - Proactive approach to identifying and addressing issues, with a focus on ownership and accountability.\n\n\n\n\nBONUS POINTS IF YOU HAVE:\n\n - Experience with Apache Airflow\n\n - Experience working on a SaaS product\n\n\n\nThe estimated salary for this role ranges from $150,000 - $165,000 based on leveling and geography, along with an equity component and a comprehensive benefits package. This range is merely an estimate; actual compensation may deviate from this range based on skills, experience, and qualifications.\n\n\n\n#LI-Fulltime\n\n#LI-Hybrid\n\n\n\nAt Astronomer, we value diversity. We are an equal opportunity employer: we do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.","salary_min":150000,"salary_max":165000,"location":"New York, NY","workplace":"hybrid","job_type":"full-time","experience_level":"mid","tags":["cloud","distributed-systems","data-pipeline","infrastructure"],"apply_url":"https://jobs.ashbyhq.com/astronomer/01ef4c2a-cc88-4488-9597-bab3933bbfab/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-05-22T11:07:07.074Z","expires_at":"2026-06-29T14:16:40.346071Z","created_at":"2026-05-27T14:17:27.853576Z","updated_at":"2026-05-30T14:16:40.458234Z","company_name":"Astronomer","company_slug":"astronomer","company_logo_url":"https://www.google.com/s2/favicons?domain=astronomer.io\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/de07a07f-bf9b-404c-afb9-f5c999826ed8"}],"page":1,"per_page":20,"total":1498,"total_pages":75}