{"access":{"catalog_url":"https://aidevboard.com/api/v1/catalog","description":"Public read endpoints are open and free. 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We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the role \n We are seeking an experienced Revenue Systems Engineering Director to join our Finance Systems team at Anthropic. You will own the technical architecture, implementation, and optimization of our Order-to-Cash (OTC) systems as we scale globally. You'll serve as the technical lead for our revenue systems within the ERP ecosystem, driving automation of revenue recognition processes and integrating our billing, sales, and financial systems.\n Responsibilities \n Revenue Platform Architecture \u0026 Development \n \n \n Own the revenue systems architecture and development supporting multi-entity operations and global subsidiaries\n \n Design and implement scalable data pipelines using DBT/SQL frameworks to transform high-volume financial transactions with low-latency response times for real-time integrations\n \n Build and maintain automated revenue recognition workflows ensuring ASC 606 compliance for complex subscription and consumption-based billing models\n \n Data Engineering \u0026 Pipeline Orchestration \n \n \n Modify, enhance, and optimize data transformation models using DBT/SQL to support standardized data flows across accounting, billing engineering, and product teams\n \n Establish comprehensive testing frameworks for data transformations, formally documenting successful behavior to support System Integration Testing (SIT) and audit requirements\n \n Systems Integration \u0026 Technical Leadership \n \n \n Collaborate with Revenue Accounting, Data Infrastructure, and BizTech teams to design enterprise-grade integration patterns supporting significant transaction volume growth\n \n Implement automated reconciliation engines achieving rapid variance detection and substantially reducing manual revenue team effort\n \n Provide technical expertise during month-end close processes, ensuring system reliability and performance during peak transaction volumes\n \n Innovation \u0026 Continuous Improvement \n \n \n Pioneer AI integration for revenue operations, building intelligent agents for discrepancy investigation, automated testing, and self-service analytics\n \n Evaluate and implement emerging technologies to modernize integration architecture\n \n Establish best practices with CI/CD pipelines, automated testing, and deployment workflows maintaining high reliability standards\n \n Mentor team members on technical best practices, code reviews, and documentation standards\n \n Minimum qualifications \n \n \n Possess strong technical proficiency in Python, SQL, DBT, and modern data engineering tools (workflow orchestration, infrastructure as code, cloud data warehouses)\n \n Have extensive experience with enterprise billing platforms and revenue recognition requirements (ASC 606)\n \n Demonstrate expert-level understanding of ERP systems with hands-on configuration and integration experience, including proficiency with API development (REST, SOAP/XML, webhooks) and understanding of ERP extension patterns and custom object development\n \n Experience with additional programming languages (Java, JavaScript/TypeScript, Go) for building integrations, APIs, and custom ERP extensions\n \n Track record of leading technical workstreams during ERP transformations or major system migrations\n \n Are skilled at designing scalable integration architectures, including both real-time APIs and batch processing patterns for high-volume financial transactions\n \n Have proven ability to translate complex business requirements into robust technical solutions while maintaining alignment with accounting principles and audit standards\n \n Understanding of consumption-based pricing models, usage metering platforms, and marketplace billing\n \n Thrive in a fast-paced, high-growth environment where you'll balance innovation with operational stability\n \n Have excellent communication skills to bridge technical and business stakeholders, including Finance, Accounting, Revenue Operations, and Engineering teams\n \n Preferred qualifications \n \n Have 12+ years of experience in revenue systems engineering, with deep hands-on expertise in Order-to-Cash (OTC) implementations\n \n \n \n Experience implementing revenue platforms at scale for SaaS or subscription-based businesses processing high transaction volumes\n \n Background in financial systems implementations supporting multi-entity, multi-currency operations with complex revenue recognition scenarios\n \n Hands-on experience with CRM/CPQ platforms and integration patterns connecting sales systems to billing and ERP systems\n \n Specific experience with: Salesforce CPQ/Revenue Cloud, Zuora, Stripe Billing,, Workday Financials\n \n Familiarity with Workday Prism and Oracle Accounting Hub solutions for managing third-par","salary_min":270000,"salary_max":315000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["payments","llm","agents","api-design","alignment","data-pipeline"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5409055008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-29T06:34:37Z","expires_at":"2026-09-29T13:30:17.242138Z","created_at":"2026-08-29T13:30:18.244908Z","updated_at":"2026-08-30T13:30:17.394388Z","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/74bd7349-e7f3-4d98-a3f0-ba2a67cb91ec"},{"id":"c0509020-b00e-47f4-9cd5-7680b67c5c2f","company_id":"219030bb-3e37-4376-be76-0ea3c447e4b2","title":"Research Quality Analyst","slug":"research-quality-analyst-97f71c69","description":"About AlphaSense:  \n The world’s most sophisticated companies rely on AlphaSense to remove uncertainty from decision-making. With market intelligence and search built on proven AI, AlphaSense delivers insights that matter from content you can trust. Our universe of public and private content includes equity research, company filings, event transcripts, expert calls, news, trade journals, and clients’ own research content. \n The acquisition of Tegus by AlphaSense in 2024 advances our shared mission to empower professionals to make smarter decisions through AI-driven market intelligence. Together, AlphaSense and Tegus will accelerate growth, innovation, and content expansion, with complementary product and content capabilities that enable users to unearth even more comprehensive insights from thousands of content sets. Our platform is trusted by over 6,000 enterprise customers, including a majority of the S\u0026P 500. Founded in 2011, AlphaSense is headquartered in New York City with more than 2,000 employees across the globe and offices in the U.S., U.K., Finland, India, Singapore, Canada, and Ireland. Come join us! \n About Expert Insights: \n Expert Insights, which spans AlphaSense’s Expert Transcript Library and 1x1 Call Services offerings,  delivers a new and transformative form of market intelligence content. Through transcripts covering thousands of companies, it captures the unfiltered views and insights of business operators in the trenches, interviewed by professional investors who drill into key questions on what’s truly important about a company at each moment in time. AlphaSense’s library of over 220,000 transcripts is the market’s largest, covering all sectors of the economy, with thousands more published each month. Expert Insights is quickly becoming a table-stakes solution for institutional investors to choose the right companies to invest in while gaining rapid adoption among all other consumers of market intelligence from sell-side research and banking, consultancies, and large corporations.\n About the Team: \n The Directed Content Team is an integral part of the Expert Insights group, generating thousands of calls each quarter on high-value, strategic content targets — ensuring that the AlphaSense Expert Transcript Library (ETL) delivers comprehensive coverage to our users. The Directed Content team is responsible for identifying, recruiting, and onboarding the best possible experts from around the world based on the targets and topics we are looking to generate content against — ensuring that the interviews with those experts are of the highest possible quality and relevance.\n About the Role:  \n As the Research Quality Analyst on the Directed Content (DC) team, you will provide quality control across a continuous, high-volume research pipeline. This role uses sampling across the project lifecycle to catch quality issues, identify patterns, and drive improvements to process and AI prompting that raise the overall quality and consistency of DC's research output. You will be a key player in ensuring that our research is accurate, consistent, and valuable to our clients. This position offers an opportunity to develop expertise in AI-powered research and to contribute to refining workflows that generate trackable, high-quality insights.\n What You’ll Do:  \n \n Sample and review research projects at multiple stages — prompt sheets, knowledge bases, and published expert transcripts — to catch quality issues both before and after publication.\n Identify patterns in quality driven by process gaps or issues.\n Test and help deploy process and prompt fixes that address those patterns, so quality review becomes faster and narrower in scope over time.\n Assist in refining and iterating on AI prompts.\n Help develop and maintain QA checklists and documentation, standardizing quality practices.\n Track and analyze quality metrics, reporting on trends, and recommending process improvements.\n Support testing of new AI-driven tools and workflows as they're introduced to Directed Content.\n \n Who You Are: \n \n You have 1–3 years of experience in a role focused on quality assurance, content review, copy editing, research, or data analysis.\n You possess an exceptional eye for detail and a commitment to producing high-quality work.\n You are highly analytical and intellectually curious, with the ability to quickly understand new concepts and identify inconsistencies in data and text.\n You have excellent written and verbal communication skills, with experience editing and proofreading professional documents.\n You are comfortable working collaboratively in a fast-paced, cross-functional environment.\n You have a strong interest in technology and artificial intelligence; experience with AI or large language models is a significant plus.\n For base compensation, we set standard ranges for all roles based on function and level benchmarked against similar stage growth companies and internal comparables. In ","salary_min":62000,"salary_max":77000,"location":"Chicago, IL","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["payments","llm","data-pipeline","search","research"],"apply_url":"https://job-boards.greenhouse.io/alphasense/jobs/8692344002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-28T20:38:01Z","expires_at":"2026-09-29T13:40:52.736324Z","created_at":"2026-08-29T13:41:39.658903Z","updated_at":"2026-08-30T13:40:52.870924Z","company_name":"AlphaSense","company_slug":"alphasense","company_logo_url":"https://www.google.com/s2/favicons?domain=alpha-sense.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/c0509020-b00e-47f4-9cd5-7680b67c5c2f"},{"id":"55f8d3f4-58f6-4ca2-9b49-1e84deeaec13","company_id":"e8c9f3a5-9310-43f5-9341-321fe6d93a92","title":"Triage Automation Engineer","slug":"triage-automation-engineer-90734211","description":"About us    \n Founded in 2017, Wayve is the leading developer of Embodied AI technology.  Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems.\n Our vision is to create autonomy that propels the world forward.  Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving.  In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future.\n At Wayve, your contributions matter.  We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact.  \n Make Wayve the experience that defines your career!  \n The role  \n As a Triage Automation Engineer at Wayve , you'll play a key role in scaling our Triage and Detectives workflow through process improvement, tooling, and automation. You'll partner with Triage Specialists and Detective Engineers to turn repeated, manual analysis into reliable, productised automation, including triage bots, behavioural classifiers, and workflow tooling. That reduces manual load and speeds up how quickly we identify, understand, and resolve issues across our test and on-road fleets.\n This is a highly collaborative, detail-oriented role with a direct impact on the safety and efficiency of Wayve's development pipeline.\n Key responsibilities:\n \n Document and measure existing Triage and Detective workflows to identify opportunities for automation and process improvement\n Prioritise improvements based on cost, benefit, impact, and feasibility\n Partner with Detective Engineers to integrate their scripts and tools into scalable, productised workflows\n Work with development, ML, and AI teams to deliver the tooling improvements Triage needs\n Build automation pipelines — including triage bots and behavioural classifiers — that reduce manual load on Triage Specialists\n Validate automation changes and outputs, including the accuracy and reliability of classifications and suggested root causes\n Document newly implemented automations: what they do, how they work, how to use them, known limitations, and expected outputs\n Measure and report on triage quality and throughput to track the impact of automation\n Collaborate with senior management and cross-functional stakeholders to shape the roadmap for business-critical automation\n Willingness to travel domestically and internationally (including trips to our London office)\n \n About you   \n In order to set you up for success as a Triage Automation Engineer at Wayve, we’re looking for the following skills and experience.  \n Essential \n \n 3+ years of experience working with complex systems, ideally within robotics or autonomous vehicles\n Strong scripting and analytical skills (e.g. Python, SQL, Bash/Shell)\n Hands-on experience operating in a remote Linux environment\n Experience building or maintaining data pipelines or notebooks (e.g. Databricks, Jupyter)\n Great communication skills, able to explain complex technical problems to both technical and non-technical stakeholders\n Expertise using issue tracking and configuration management tools such as Jira, Confluence, and Bitbucket/GitLab\n Comfort with ambiguity — able to measure an existing workflow, identify where automation adds value, and scope a sensible solution\n \n Desirable \n \n Experience with web development languages (e.g. HTML, CSS, React, Java) for building internal tooling\n Practical experience with machine learning or classification models (e.g. PyTorch)\n Experience with cloud services (ideally Microsoft Azure)\n Passion for taking research ideas to production\n Track record of promoting statistical rigour and experimental best practice\n Experience working in a fast-moving tech company or startup\n \n This is a full-time role based in our office in Sunnyvale.  At Wayve we want the best of all worlds so we operate a hybrid working policy that combines time together in our offices and workshops to fuel innovation, culture, relationships and learning, and time spent working from home. The reasonably estimated salary for this role ranges from $144,500–$183,200, plus a competitive equity package. Actual compensation is based on the candidate's skills, qualifications, and experience.\n  \n Wayve is committed to creating an inclusive interview experience. If you require any accommodations or adjustments to participate fully in our interview process, please let us know. \n We understand that everyone has a unique set of skills and experiences and that not everyone will meet all of the requirements listed above. If you’re passionate about self-driving cars and think you have what it t","salary_min":144500,"salary_max":183200,"location":"Sunnyvale, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["autonomous-vehicles","generative-ai","robotics","data-pipeline","pytorch","evaluation"],"apply_url":"https://wayve.firststage.co/jobs?gh_jid=8756182002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-28T17:56:55Z","expires_at":"2026-09-29T13:43:31.521698Z","created_at":"2026-08-29T13:44:35.170073Z","updated_at":"2026-08-30T13:43:31.651707Z","company_name":"Wayve","company_slug":"wayve","company_logo_url":"https://www.google.com/s2/favicons?domain=wayve.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/55f8d3f4-58f6-4ca2-9b49-1e84deeaec13"},{"id":"57b56d5b-e1f9-4118-afb8-2bd1f37d7f46","company_id":"332b7698-676b-4a3e-8b02-81b1195c5af6","title":"Sr. AI Engineer - FDE (Forward Deployed Engineer) - U.S. Federal Sector","slug":"sr-ai-engineer-fde-forward-deployed-engineer-us-federal-sector-2b0534c9","description":"PLEASE NOTE : Due to federal contract requirements and client site access obligations, U.S. citizenship and eligibility for a U.S. government secret clearance are required to access classified information. The position is based in the Washington, D.C., Maryland, or Virginia metropolitan area and includes periodic on‑site work and client collaboration. Candidates with an active Secret or higher clearance are strongly encouraged to apply. \n The AI Forward Deployed Engineering (AI FDE) team is a highly specialized customer-facing AI team at Databricks. We deliver professional services engagements to help our customers build and productionize first-of-its-kind AI applications. We work cross-functionally to shape long-term strategic priorities and initiatives alongside engineering, product, and developer relations, as well as support internal subject matter expert (SME) teams. We view our team as an ensemble: we look for individuals with strong, unique specializations to improve the overall strength of the team. This team is the right fit for you if you love working with customers, teammates, and fueling your curiosity for the latest trends in GenAI, LLMOps, and ML more broadly. This role can be remote.\n The impact you will have: \n \n Develop cutting-edge GenAI solutions, incorporating the latest techniques from Databricks AI research to solve customer problems\n Own production rollouts of consumer and internally facing GenAI applications\n Serve as a trusted technical advisor to customers across a variety of domains\n Present at conferences such as Data + AI Summit , recognized as a thought leader internally and externally\n Collaborate cross-functionally with the product and engineering teams to influence priorities and shape the product roadmap \n \n What we look for: \n \n Experience building GenAI applications, including RAG, multi-agent systems, Text2SQL, fine-tuning, etc., with tools such as HuggingFace, LangChain, and DSPy\n Expertise in deploying production-grade GenAI applications, including evaluation and optimizations \n Extensive years of hands-on industry data science experience, leveraging common machine learning and data science tools, i.e. pandas, scikit-learn, PyTorch, etc.\n Experience building production-grade machine learning deployments on AWS, Azure, or GCP\n Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience\n Experience communicating and/or teaching technical concepts to non-technical and technical audiences alike\n Passion for collaboration, life-long learning, and driving business value through AI\n [Preferred] Experience using the Databricks Intelligence Platform and Apache Spark™ to process large-scale distributed datasets\n Willing to travel once every 4-8 weeks to see customers (as needed)\n  \n Pay Range Transparency \n Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles.  Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here . \n  \n Local Pay Range\n $182,000 — $250,208 USD \n About Databricks \n Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT\u0026T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn , X , YouTube , and Instagram . Benefits At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here . \n Our Commitment to Diversity and Inclusion \n At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affili","salary_min":182000,"salary_max":250208,"location":"Washington, DC","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["pytorch","data-pipeline","fine-tuning","llm","generative-ai","agents"],"apply_url":"https://databricks.com/company/careers/open-positions/job?gh_jid=8760168002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-28T14:56:34Z","expires_at":"2026-09-29T13:32:32.139503Z","created_at":"2026-08-29T13:32:27.824973Z","updated_at":"2026-08-30T13:32:32.280262Z","company_name":"Databricks","company_slug":"databricks","company_logo_url":"https://www.google.com/s2/favicons?domain=databricks.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/57b56d5b-e1f9-4118-afb8-2bd1f37d7f46"},{"id":"11b73365-9170-4b94-a834-6cf9ce41c2db","company_id":"332b7698-676b-4a3e-8b02-81b1195c5af6","title":"Sr. AI Engineer - FDE (Forward Deployed Engineer) - U.S. Federal Sector","slug":"sr-ai-engineer-fde-forward-deployed-engineer-us-federal-sector-3fa2b5eb","description":"PLEASE NOTE : Due to federal contract requirements and client site access obligations, U.S. citizenship and eligibility for a U.S. government secret clearance are required to access classified information. The position is based in the Washington, D.C., Maryland, or Virginia metropolitan area and includes periodic on‑site work and client collaboration. Candidates with an active Secret or higher clearance are strongly encouraged to apply. \n The AI Forward Deployed Engineering (AI FDE) team is a highly specialized customer-facing AI team at Databricks. We deliver professional services engagements to help our customers build and productionize first-of-its-kind AI applications. We work cross-functionally to shape long-term strategic priorities and initiatives alongside engineering, product, and developer relations, as well as support internal subject matter expert (SME) teams. We view our team as an ensemble: we look for individuals with strong, unique specializations to improve the overall strength of the team. This team is the right fit for you if you love working with customers, teammates, and fueling your curiosity for the latest trends in GenAI, LLMOps, and ML more broadly. This role can be remote.\n The impact you will have: \n \n Develop cutting-edge GenAI solutions, incorporating the latest techniques from Databricks AI research to solve customer problems\n Own production rollouts of consumer and internally facing GenAI applications\n Serve as a trusted technical advisor to customers across a variety of domains\n Present at conferences such as Data + AI Summit , recognized as a thought leader internally and externally\n Collaborate cross-functionally with the product and engineering teams to influence priorities and shape the product roadmap \n \n What we look for: \n \n Experience building GenAI applications, including RAG, multi-agent systems, Text2SQL, fine-tuning, etc., with tools such as HuggingFace, LangChain, and DSPy\n Expertise in deploying production-grade GenAI applications, including evaluation and optimizations \n Extensive years of hands-on industry data science experience, leveraging common machine learning and data science tools, i.e. pandas, scikit-learn, PyTorch, etc.\n Experience building production-grade machine learning deployments on AWS, Azure, or GCP\n Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research, etc.) or equivalent practical experience\n Experience communicating and/or teaching technical concepts to non-technical and technical audiences alike\n Passion for collaboration, life-long learning, and driving business value through AI\n [Preferred] Experience using the Databricks Intelligence Platform and Apache Spark™ to process large-scale distributed datasets\n Willing to travel once every 4-8 weeks to see customers (as needed)\n  \n Pay Range Transparency \n Databricks is committed to fair and equitable compensation practices. The pay range(s) for this role is listed below and represents the expected salary range for non-commissionable roles or on-target earnings for commissionable roles.  Actual compensation packages are based on several factors that are unique to each candidate, including but not limited to job-related skills, depth of experience, relevant certifications and training, and specific work location. Based on the factors above, Databricks anticipates utilizing the full width of the range. The total compensation package for this position may also include eligibility for annual performance bonus, equity, and the benefits listed above. For more information regarding which range your location is in visit our page here . \n  \n Local Pay Range\n $182,000 — $250,208 USD \n About Databricks \n Databricks is the Data and AI company. More than 20,000 organizations worldwide — including adidas, AT\u0026T, Bayer, Block, Mastercard, Rivian, Unilever, and 70% of the Fortune 500 — rely on the Databricks Data + AI Platform to build and scale data and AI apps, analytics and agents. Headquartered in San Francisco with 30+ offices around the globe, Databricks offers a unified platform that includes Genie, Lakebase, Agent Bricks, Lakeflow, Lakehouse, and Unity Catalog. To learn more, follow Databricks on LinkedIn , X , YouTube , and Instagram . Benefits At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here . \n Our Commitment to Diversity and Inclusion \n At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affili","salary_min":182000,"salary_max":250208,"location":"Virginia","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["pytorch","fine-tuning","data-pipeline","agents","llm","generative-ai"],"apply_url":"https://databricks.com/company/careers/open-positions/job?gh_jid=8760167002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-28T14:56:31Z","expires_at":"2026-09-29T13:32:32.232912Z","created_at":"2026-08-29T13:32:27.639904Z","updated_at":"2026-08-30T13:32:32.374987Z","company_name":"Databricks","company_slug":"databricks","company_logo_url":"https://www.google.com/s2/favicons?domain=databricks.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/11b73365-9170-4b94-a834-6cf9ce41c2db"},{"id":"0223cfe4-98fd-4376-8efe-3be62e75ecb8","company_id":"76c63eb7-c307-4322-8c2b-c20216feec49","title":"Senior Engineer, Back-End (Security Controls)","slug":"senior-engineer-back-end-security-controls-b79d69e4","description":"Get to Know Us\n\nHorizon3 is a fast-growing, remote cybersecurity company dedicated to the mission of enabling organizations to proactively find and fix and verify exploitable attack vectors before criminals exploit them. Our flagship product, the NodeZeroTM platform, delivers production-safe autonomous pentests and other key assessment operations that scale across the largest internal, external, cloud, and hybrid cloud environments. NodeZero has been adopted by organizations of all sizes, from small educational institutions to government agencies and Global 100 enterprises. It is used by ITOps/SecOps teams, consulting pentesters, and MSSPs and MSPs. \n\nWe are a fusion of former U.S. Special Operations cyber operators, startup engineers, and formerly frustrated cybersecurity practitioners. We're committed to helping solve our common security problems: ineffective security tools, false positives resulting in alert fatigue, blind spots, \"checkbox” security culture, cybersecurity skills shortage, and the long lead time and expense of hiring outside consultants. Collectively, we are a team of learn it alls, committed to a culture of respect, collaboration, ownership, and results.\n\n \n\n\nROLE OVERVIEW:\n\nThe Senior Backend Engineer will join our world-class software engineering team as a key contributor to both user-facing product launches and platform enhancements and stability. You will own our security controls integration platform, abstracting the complexity of the EDR ecosystem from the rest of our system. Core responsibilities include developing per-vendor normalizers, building live API pipelines, defining internal schemas, and managing versioning, deprecations, fault tolerance, and integration health monitoring. You'll also collaborate with the broader backend engineering team on cross-cutting scale and reliability work.\n\n\n\n\nWHAT YOU’LL DO\n\nResponsibilities\n\n - Design and build dedicated normalizers per EDR vendor, with the testing rigor to keep them trustworthy as vendor APIs evolve\n\n - Develop core product features in ETL (Extract, Transform, Load) and GraphQL to support data processing and retrieval.\n\n - Design and implement backend APIs in GraphQL to facilitate data interactions between different components of the product.\n\n - Build and maintain ETL pipelines for efficient data processing and analytics.\n\n - Collaborate with frontend team members to present data in a clear and user-friendly format, enhancing the product's user experience.\n   \n\n\nWHAT YOU’LL BRING\n\n - Deep experience building and maintaining third-party API integrations at scale\n\n - Hands-on familiarity with EDR platforms and an understanding of how SOC teams consume EDR data\n\n - Driven, self-managed, capable of conceiving and implementing solutions on your own and with a team\n\n - A strong desire to continuously improve and learn new technologies in a fast-paced Agile development environment\n\n - Excellent analytical and problem-solving skills, effective communication, attention to detail and high-quality work\n\n - Strong technical documentation ability to support the development process.\n\n - Effective communication and collaboration with designers, developers, and product managers.\n\n - Ability and interest in mentoring junior and mid-level engineers to foster their growth and development.\n   \n\n\nREQUIRED EDUCATION / EXPERIENCE\n\n - Bachelor's Degree in Computer Science, Computer Engineering or related field.\n\n - 7+ yrs professional software engineering experience using modern object-oriented or functional languages (Python, Java, Go, Scala, C++, TypeScript, etc).\n\n - Experience building applications on cloud computing platforms such as AWS, Azure, GCP, using container technologies such as Docker and Kubernetes.\n\n - Expert proficiency in SQL.\n   \n\n\nPREFERRED EDUCATION/EXPERIENCE\n\n - Experience with various database architectures including relational (PostgreSQL) and graph (Neo4j).\n\n - Experience building GraphQL backends.\n\n - Experience managing and maintaining production infrastructure.\n\n - Experience building platform capabilities for internal teams.\n\n \n\nPerks of Horizon3\n\n - Inclusive Team: We value diversity and promote an inclusive culture where everyone can thrive.\n\n - Growth Opportunities: Be part of a dynamic and growing team with numerous career development opportunities.\n\n - Innovative Culture: Work in a collaborative environment that encourages creativity and out-of-the-box thinking.\n\n - Hybrid \u0026 Remote Work: We embrace a mix of remote and hybrid work models depending on role and location, including our Chicago office, where some roles require regular in-office presence.\n\n - Competitive Compensation: We offer competitive salary, equity and benefits. Our benefits include health, vision \u0026 dental insurance for you and your family, a flexible vacation policy, and generous parental leave.\n\n\n\nCompensation and Values\n\nAt Horizon3, we believe that our people are our greatest asset, and our compensation philosophy reflects this core va","salary_min":199750,"salary_max":270250,"location":"Remote (US)","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["api-design","security","data-pipeline","backend"],"apply_url":"https://jobs.ashbyhq.com/horizon3ai/7305c82f-fddd-4e11-a539-34a401cc9cc5/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-27T17:11:14.423Z","expires_at":"2026-09-29T13:36:42.868673Z","created_at":"2026-08-29T13:37:07.081074Z","updated_at":"2026-08-30T13:36:43.001684Z","company_name":"Horizon3 AI","company_slug":"horizon3-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=horizon3.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/0223cfe4-98fd-4376-8efe-3be62e75ecb8"},{"id":"7aa92365-6b3e-4f51-a168-2674aa7d8a06","company_id":"76c63eb7-c307-4322-8c2b-c20216feec49","title":"Senior Data Infrastructure Engineer","slug":"senior-data-infrastructure-engineer-96f0f9f8","description":"Get to Know Us\n\nHorizon3 is a fast-growing, remote cybersecurity company dedicated to the mission of enabling organizations to proactively find and fix and verify exploitable attack vectors before criminals exploit them. Our flagship product, the NodeZeroTM platform, delivers production-safe autonomous pentests and other key assessment operations that scale across the largest internal, external, cloud, and hybrid cloud environments. NodeZero has been adopted by organizations of all sizes, from small educational institutions to government agencies and Global 100 enterprises. It is used by ITOps/SecOps teams, consulting pentesters, and MSSPs and MSPs. \n\nWe are a fusion of former U.S. Special Operations cyber operators, startup engineers, and formerly frustrated cybersecurity practitioners. We're committed to helping solve our common security problems: ineffective security tools, false positives resulting in alert fatigue, blind spots, \"checkbox” security culture, cybersecurity skills shortage, and the long lead time and expense of hiring outside consultants. Collectively, we are a team of learn it alls, committed to a culture of respect, collaboration, ownership, and results.\n\nWe are seeking a Senior Data Infrastructure Engineer to design, build, and own the data platform underneath our business-critical pipelines. Today the team supports account provisioning, product analytics, and the pipelines feeding our warehouse and Tableau reporting. You will help expand this into a globally deployed data platform spanning the warehouse, orchestration, and BI across multiple regions\n\nWhat You’ll Do\n\n - Design, build, and maintain business-critical data pipelines, including data ingestion and AWS Glue/Spark jobs feeding the warehouse and Tableau.\n\n - Build and operate core data infrastructure: the data warehouse, orchestration, and BI/reporting platform, deployed across multiple regions.\n\n - Help evaluate and shape our warehouse strategy, including lakehouse platforms such as Databricks.\n\n - Define and evolve event-driven contracts between producer and consumer teams, reasoning through batch vs. streaming, CDC, reprocessing, and multi-tenant/multi-region trade-offs.\n\n - Design and evolve warehouse schemas and data models.\n\n - Build observability into pipelines and platform components.\n\n - Drive work across producer and consumer teams, communicate clearly, and own outcomes end to end.\n\nWhat You’ll Bring\n\n - Strong software engineering craft: clean, correct, maintainable code with solid data-structure and problem-solving fundamentals.\n\n - Deep understanding of distributed systems and data architecture: pipeline and platform design, batch vs. streaming, CDC, event contracts, multi-tenant and multi-region trade-offs, reprocessing, and observability.\n\n - Fluent SQL and strong data-modeling skills, including designing schemas for a warehouse your team owns.\n\n - Experience owning production data infrastructure end to end.\n\n - Strong collaboration and ownership across dependent teams.\n\n - 7+ years of professional experience in data engineering, infrastructure engineering, or backend software engineering.\n\n - Bachelor’s degree in Computer Science or a related field, or equivalent practical experience.\n\n\n\nRequired Tech Stack Experience\n\n - AWS data stack, including Glue and Spark.\n\n - Cloud data warehouse or lakehouse platforms such as Amazon Redshift or Databricks.\n\n - Tableau or a comparable BI platform.\n\n - SQL and Python.\n\n - Workflow orchestration tooling such as Apache Airflow or Dagster\n\n \n\nPerks of Horizon3\n\n - Inclusive Team: We value diversity and promote an inclusive culture where everyone can thrive.\n\n - Growth Opportunities: Be part of a dynamic and growing team with numerous career development opportunities.\n\n - Innovative Culture: Work in a collaborative environment that encourages creativity and out-of-the-box thinking.\n\n - Hybrid \u0026 Remote Work: We embrace a mix of remote and hybrid work models depending on role and location, including our Chicago office, where some roles require regular in-office presence.\n\n - Competitive Compensation: We offer competitive salary, equity and benefits. Our benefits include health, vision \u0026 dental insurance for you and your family, a flexible vacation policy, and generous parental leave.\n\n\n\nCompensation and Values\n\nAt Horizon3, we believe that our people are our greatest asset, and our compensation philosophy reflects this core value. We are committed to fostering an environment where all employees feel valued, respected, and rewarded for their contributions. Our compensation structure is designed to be fair, competitive, and transparent, ensuring that every team member is recognized and compensated equitably across roles, levels, and locations.\n\nIn accordance with various State’s transparency regulations, we provide the following salary range information for this position:\n\n - Base salary range: $202,190 - $237,870 annually. The exact salary will be determined based on","salary_min":202190,"salary_max":237870,"location":"Remote (US)","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["data-pipeline","cloud","distributed-systems","security","infrastructure"],"apply_url":"https://jobs.ashbyhq.com/horizon3ai/4651e6b3-a8b5-484f-ac87-5b6ca04581d1/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-27T16:58:31.284Z","expires_at":"2026-09-29T13:36:43.052847Z","created_at":"2026-08-29T13:37:07.314282Z","updated_at":"2026-08-30T13:36:43.188509Z","company_name":"Horizon3 AI","company_slug":"horizon3-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=horizon3.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/7aa92365-6b3e-4f51-a168-2674aa7d8a06"},{"id":"bd52fe7c-5d97-4e82-bec6-4431216e869e","company_id":"053355fc-0162-4bb9-b414-cbf7679ee9c8","title":"Senior/Staff FDE - Synthetic Data Generation","slug":"seniorstaff-fde-synthetic-data-generation-9477fabd","description":"About Snorkel \n At Snorkel, we believe meaningful AI doesn’t start with the model, it starts with the data.\n We’re on a mission to help enterprises transform expert knowledge into specialized AI at scale. The AI landscape has gone through incredible changes since 2015, when Snorkel started as a research project in the Stanford AI Lab, to the generative AI breakthroughs of today. But one thing has remained constant: the data you use to build AI is the key to achieving differentiation, high performance, and production-ready systems. We work with some of the world’s largest organizations to empower scientists, engineers, financial experts, product creators, journalists, and more to build custom AI with their data faster than ever before. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler!\n \n About the Role\n Snorkel AI is hiring a Forward Deployed Engineer focused on Synthetic Data Generation to partner with leading AI labs and enterprises on their most critical AI initiatives.\n In this role, you will lead the technical execution of complex customer engagements where synthetic data is used to improve model training, evaluation, and performance. You will translate ambiguous model and data challenges into effective data strategies, build scalable generation and evaluation pipelines, and use experimentation to continuously improve data quality and downstream model outcomes.\n You will work across the full delivery lifecycle—from technical discovery and solution design through implementation, evaluation, and production delivery. You will also identify patterns across engagements and turn successful approaches into reusable capabilities, technical standards, and product improvements.\n Main Responsibilities\n Synthetic Data Generation \u0026 Evaluation\n \n Design and build scalable synthetic data generation, transformation, filtering, and evaluation pipelines for complex AI use cases\n Translate model objectives, failure modes, and data gaps into synthetic data strategies, experiments, and technical specifications\n Develop LLM- and ML-assisted workflows to generate high-quality training and evaluation datasets across targeted behaviors, domains, and edge cases\n Build automated evaluators, quality checks, and measurement frameworks to assess correctness, relevance, diversity, coverage, and adherence to customer requirements\n Design and run experiments to measure the impact of synthetic data on downstream model performance and iteratively improve generation approaches\n Package and deliver production-grade datasets with standardized formats, quality assurance, and clear documentation\n \n Forward Deployed Engineering \u0026 Customer Partnership\n \n Lead technical workstreams from initial solution design through production delivery, navigating ambiguity and making sound technical decisions\n Build, refine, and iterate on solutions that address customer needs, incorporating feedback to ensure the delivered work provides tangible value\n Rapidly prototype and productionize solutions across models, data pipelines, APIs, and custom applications\n Communicate technical tradeoffs, experimental results, and recommendations clearly to technical and cross-functional stakeholders\n Serve as a trusted technical partner to customers and internal delivery teams, resolving complex blockers and driving alignment\n \n Technical Leadership \u0026 Scale\n \n Identify recurring patterns across customer engagements and turn successful solutions into reusable pipelines, evaluators, tooling, and best practices\n Define and improve technical standards for synthetic data generation, experimentation, evaluation, and delivery\n Partner with DaaS Engineering and Product teams to influence platform and product capabilities based on real-world customer needs\n Lead technical design reviews, share expertise, and provide guidance to other engineers\n Stay current with emerging synthetic data, LLM evaluation, and data curation techniques and assess their applicability to customer problems\n \n What We're Looking For\n \n 5+ years of experience in machine learning engineering, data science, applied AI, forward deployed engineering, or a similar technical role\n Strong Python skills and experience building reliable production data or ML systems, including containerizing with Docker and deploying on cloud platforms (e.g., AWS, GCP, or Azure)\n Hands-on experience with LLMs—building model-based applications and data workflows with the modern GenAI/LLM stack, and integrating systems, models, and data sources through APIs\n Strong understanding of ML experimentation and evaluation, including defining metrics and using empirical results to guide technical decisions\n Experience building synthetic data, data augmentation, or model-generated training and evaluation datasets\n Experience with LLM evaluation techniques, including LLM-as-a-judge, model-based evaluation, rubric-based evaluation, or custom evaluators\n Demonstrated ability to take ambiguous technical probl","salary_min":180000,"salary_max":320000,"location":"New York, NY","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["data-pipeline","llm","reinforcement-learning","agents","generative-ai","fine-tuning"],"apply_url":"https://job-boards.greenhouse.io/snorkelai/jobs/6167063004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-27T16:56:44Z","expires_at":"2026-09-29T13:34:01.344588Z","created_at":"2026-08-29T13:34:10.967098Z","updated_at":"2026-08-30T13:34:01.484225Z","company_name":"Snorkel AI","company_slug":"snorkel-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=snorkel.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/bd52fe7c-5d97-4e82-bec6-4431216e869e"},{"id":"99ca5606-9232-4b96-8b12-b49baec86bf5","company_id":"053355fc-0162-4bb9-b414-cbf7679ee9c8","title":"Senior/Staff FDE - CUA","slug":"seniorstaff-fde-cua-6da3e68e","description":"About Snorkel \n At Snorkel, we believe meaningful AI doesn’t start with the model, it starts with the data.\n We’re on a mission to help enterprises transform expert knowledge into specialized AI at scale. The AI landscape has gone through incredible changes since 2015, when Snorkel started as a research project in the Stanford AI Lab, to the generative AI breakthroughs of today. But one thing has remained constant: the data you use to build AI is the key to achieving differentiation, high performance, and production-ready systems. We work with some of the world’s largest organizations to empower scientists, engineers, financial experts, product creators, journalists, and more to build custom AI with their data faster than ever before. Excited to help us redefine how AI is built? Apply to be the newest Snorkeler!\n About the Role\n Snorkel AI is hiring a Forward Deployed Engineer focused on Computer Use Agents to partner with leading AI labs and enterprises on their most critical agentic-AI initiatives.\n In this role, you will lead the technical execution of complex customer engagements involving agents that operate computers, browsers, and software environments to complete realistic, multi-step tasks. You will translate ambiguous product and model challenges into robust task environments, datasets, evaluators, and delivery plans that improve agent reliability and downstream performance.\n You will work across the full delivery lifecycle—from technical discovery and solution design through implementation, evaluation, and production delivery. You will also identify patterns across engagements and turn successful approaches into reusable capabilities, technical standards, and product improvements.\n Main Responsibilities\n Computer Use Agents, Data, and Evaluation\n \n Design and build task environments, datasets, and evaluation workflows for computer-using agents operating across browsers, desktop applications, terminals, and other software interfaces\n Translate customer goals, agent failure modes, and real-world workflows into representative, multi-step tasks with clear success criteria\n Develop data-generation, validation, and quality-assurance pipelines for multimodal and agentic training and evaluation data\n Build automated evaluators, checks, and measurement frameworks to assess task completion, correctness, robustness, efficiency, and adherence to requirements\n Diagnose agent failures across planning, tool use, perception, state management, and interaction with user interfaces; turn findings into improved tasks, data, and evaluations\n Design and run experiments to measure how data, task design, and evaluation changes affect downstream agent performance\n Deliver reusable, production-grade task suites, datasets, and evaluation assets that help customers train, benchmark, and improve computer-use agents\n \n Forward Deployed Engineering \u0026 Customer Partnership\n \n Lead technical workstreams from initial solution design through production delivery, navigating ambiguity and making sound technical decisions\n Build, refine, and iterate on solutions that address customer needs, incorporating feedback to ensure the delivered work provides tangible value\n Rapidly prototype and productionize solutions across models, agent frameworks, APIs, browser or desktop environments, and custom applications\n Communicate technical tradeoffs, experimental results, and recommendations clearly to technical and cross-functional stakeholders\n Serve as a trusted technical partner to customers and internal delivery teams, resolving complex blockers and driving alignment\n \n Technical Leadership \u0026 Scale\n \n Identify recurring patterns across customer engagements and turn successful solutions into reusable task frameworks, evaluators, tooling, and best practices\n Define and improve technical standards for agent task design, environment reliability, evaluation, and delivery\n Partner with DaaS Engineering, Research, and Product teams to influence platform and product capabilities based on real-world customer needs\n Lead technical design reviews, share expertise, and provide guidance to other engineers\n Stay current with emerging agentic-AI, computer-use, evaluation, and data-curation techniques and assess their applicability to customer problems\n \n What We're Looking For\n \n 5+ years of experience in machine learning engineering, software engineering, applied AI, forward deployed engineering, solutions engineering, or a similar technical role\n Strong Python skills and experience building reliable production software, data, or ML systems\n Hands-on experience building, evaluating, or deploying LLM-based or agentic systems, including computer-use agents (CUA)\n Strong understanding of experimentation and evaluation, including LLM-as-a-judge / model-based evaluation, defining metrics, and using empirical results to guide technical decisions\n Experience designing task environments, datasets, and verifiers for agents, including reward \u0026 verifier desi","salary_min":180000,"salary_max":320000,"location":"New York, NY","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["generative-ai","fine-tuning","reinforcement-learning","data-pipeline","agents","llm"],"apply_url":"https://job-boards.greenhouse.io/snorkelai/jobs/6167049004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-27T16:38:34Z","expires_at":"2026-09-29T13:34:01.251345Z","created_at":"2026-08-29T13:34:10.876038Z","updated_at":"2026-08-30T13:34:01.388379Z","company_name":"Snorkel AI","company_slug":"snorkel-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=snorkel.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/99ca5606-9232-4b96-8b12-b49baec86bf5"},{"id":"1cab6a2a-2b5f-4e26-b0dd-0a2b833905b0","company_id":"a0000000-0000-0000-0000-000000000001","title":"Staff+ Software Engineer, RL Data Platform","slug":"staff-software-engineer-rl-data-platform-224ae32b","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 role \n Anthropic's RL Data Platform team builds the systems that produce, move, and serve the human data Claude learns from: the interfaces humans use to give feedback, the pipelines that turn raw feedback into training signal, and the tooling researchers use to launch, monitor, and inspect data collection. Every RL run depends on a steady supply of high-quality data - human feedback, expert demonstrations, graded transcripts - and when a researcher has an idea for new data on Monday, our job is to make it collectable by Wednesday and in the training mix by Friday.\n This is a full-stack, ownership-heavy role on a small, senior team. You'll design and ship web interfaces used by thousands of expert annotators, build the backend services and data pipelines behind them, and work directly with RL researchers to understand what data they need and why. You'll scope your own projects, make architectural calls, and see them through to production. We're looking for engineers who treat researchers as their users, build for reliability first, and care as much about the shape of the data leaving the system as the UI going into it.\n Key responsibilities \n \n \n Design, build, and operate the feedback and data collection interfaces used by human annotators, domain experts, and internal researchers.\n \n Build and maintain the backend services, APIs, and pipelines that route model samples to humans and return structured feedback to training.\n \n Own the reliability, latency, and usability of systems that run continuously against live model endpoints.\n \n Partner with RL researchers to translate loosely specified data needs into well-scoped collection campaigns and the tooling to run them.\n \n Build dashboards, monitoring, and inspection tools so researchers can see data quality and throughput without asking an engineer.\n \n Identify and remove the bottlenecks between \"we want this data\" and \"it's in the training mix\".\n \n Minimum qualifications \n \n \n Strong full-stack engineering skills, with production experience in TypeScript/React on the frontend and Python on the backend.\n \n Experience designing and operating backend services and data pipelines that other teams depend on.\n \n A track record of owning projects end-to-end, from an ambiguous brief to something in production that people use.\n \n Comfort working directly with technical stakeholders whose needs change week to week, and the judgment to push back when something isn't worth building.\n \n Effective use of AI tools in your own day-to-day work.\n \n Care about the societal impacts of your work.\n \n Preferred qualifications \n \n \n Experience building annotation, labelling, evaluation, or other human-in-the-loop data tooling.\n \n Experience with RLHF, preference data, or other human-feedback pipelines for ML systems.\n \n Experience shipping researcher-facing or other expert-facing internal tools people love: interviewing users, hunting down friction, measurably improving the experience.\n \n Experience running experiments on data collection interfaces and using the results to improve data quality.\n \n Experience working with crowdworker or expert vendor platforms at scale.\n \n Familiarity with how LLMs are trained and evaluated.\n \n Representative projects \n \n \n Build an interface that lets a domain expert review a long agentic transcript, flag the step where things went wrong, and write a corrected continuation - with the result landing in a training-ready format.\n \n Rework the sampling path between our feedback interfaces and model endpoints to cut time-to-first-sample for annotators.\n \n Build a campaign launcher that lets a researcher stand up a new data collection effort (task, rubric, population, quality checks) without writing code.\n \n Instrument annotator behaviour to detect low-effort or adversarial work and surface it to the quality team automatically.\n \n Design the data model for a kind of feedback we haven't collected before, and ship the pipeline that gets it into the training mix.\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 $320,000 — $405,000 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","salary_min":320000,"salary_max":405000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","agents","data-pipeline","reinforcement-learning","alignment","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5404730008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-27T13:21:33Z","expires_at":"2026-09-29T13:30:42.210504Z","created_at":"2026-08-27T13:30:43.573151Z","updated_at":"2026-08-30T13:30:42.358845Z","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/1cab6a2a-2b5f-4e26-b0dd-0a2b833905b0"},{"id":"d9e245d1-a6c7-4071-9742-6a6ed60d8557","company_id":"a0000000-0000-0000-0000-000000000001","title":"Staff+ Research Engineer, RL Data Platform","slug":"staff-research-engineer-rl-data-platform-41e9b926","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 role \n Anthropic's RL Data Platform team builds the systems that produce, move, and serve the human data Claude learns from: the interfaces humans use to give feedback, the pipelines that turn raw feedback into training signal, and the tooling researchers use to launch, monitor, and inspect data collection. Every RL run depends on a steady supply of high-quality data - human feedback, expert demonstrations, graded transcripts - and when a researcher has an idea for new data on Monday, our job is to make it collectable by Wednesday and in the training mix by Friday.\n This is a full-stack, ownership-heavy role on a small, senior team. You'll design and ship web interfaces used by thousands of expert annotators, build the backend services and data pipelines behind them, and work directly with RL researchers to understand what data they need and why. You'll scope your own projects, make architectural calls, and see them through to production. We're looking for engineers who treat researchers as their users, build for reliability first, and care as much about the shape of the data leaving the system as the UI going into it.\n Key responsibilities \n \n \n Design, build, and operate the feedback and data collection interfaces used by human annotators, domain experts, and internal researchers.\n \n Build and maintain the backend services, APIs, and pipelines that route model samples to humans and return structured feedback to training.\n \n Own the reliability, latency, and usability of systems that run continuously against live model endpoints.\n \n Partner with RL researchers to translate loosely specified data needs into well-scoped collection campaigns and the tooling to run them.\n \n Build dashboards, monitoring, and inspection tools so researchers can see data quality and throughput without asking an engineer.\n \n Identify and remove the bottlenecks between \"we want this data\" and \"it's in the training mix\".\n \n Minimum qualifications \n \n \n Strong full-stack engineering skills, with production experience in TypeScript/React on the frontend and Python on the backend.\n \n Experience designing and operating backend services and data pipelines that other teams depend on.\n \n A track record of owning projects end-to-end, from an ambiguous brief to something in production that people use.\n \n Comfort working directly with technical stakeholders whose needs change week to week, and the judgment to push back when something isn't worth building.\n \n Effective use of AI tools in your own day-to-day work.\n \n Care about the societal impacts of your work.\n \n Preferred qualifications \n \n \n Experience building annotation, labelling, evaluation, or other human-in-the-loop data tooling.\n \n Experience with RLHF, preference data, or other human-feedback pipelines for ML systems.\n \n Experience shipping researcher-facing or other expert-facing internal tools people love: interviewing users, hunting down friction, measurably improving the experience.\n \n Experience running experiments on data collection interfaces and using the results to improve data quality.\n \n Experience working with crowdworker or expert vendor platforms at scale.\n \n Familiarity with how LLMs are trained and evaluated.\n \n Representative projects \n \n \n Build an interface that lets a domain expert review a long agentic transcript, flag the step where things went wrong, and write a corrected continuation - with the result landing in a training-ready format.\n \n Rework the sampling path between our feedback interfaces and model endpoints to cut time-to-first-sample for annotators.\n \n Build a campaign launcher that lets a researcher stand up a new data collection effort (task, rubric, population, quality checks) without writing code.\n \n Instrument annotator behaviour to detect low-effort or adversarial work and surface it to the quality team automatically.\n \n Design the data model for a kind of feedback we haven't collected before, and ship the pipeline that gets it into the training mix.\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 $500,000 — $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","salary_min":500000,"salary_max":850000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","alignment","data-pipeline","agents","search","reinforcement-learning","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5404725008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-27T13:04:41Z","expires_at":"2026-09-29T13:30:36.347344Z","created_at":"2026-08-27T13:30:36.814345Z","updated_at":"2026-08-30T13:30:36.489598Z","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/d9e245d1-a6c7-4071-9742-6a6ed60d8557"},{"id":"e9df7d6e-9243-4269-8196-cc7374752000","company_id":"63bced38-3605-4e57-99f3-e213b2d40bf3","title":"Sr. Software Engineer, Data Platform","slug":"sr-software-engineer-data-platform-56629500","description":"Opportunity Overview:  \n This is a remote-first role that may require travel to Boston, MA for new hire onboarding and occasional in-person team meetings and company events.\n We’re seeking a Senior Software Engineer to join our Data Platform team and take ownership of designing and delivering large-scale, reliable, and governed data pipelines that power analytics, operations, and clinical intelligence across Cohere Health. This role is ideal for an engineer who thrives on solving complex data challenges, mentoring others, and raising engineering standards while contributing to scalable and sustainable platform growth.\n As a Senior Software Engineer of the data platform team, you’ll operate across the end-to-end data lifecycle—from ingestion to transformation to integration—driving solutions that align with architectural best practices, governance needs, and business outcomes. You’ll partner closely with analytics engineers, architects, and product stakeholders to ensure that data products are performant, compliant, and trustworthy.\n What you’ll do: \n \n Lead the design and implementation of complex data pipelines and infrastructure across multiple domains\n Mentor junior engineers, sharing expertise and raising team-wide engineering standards\n Implement and enforce data quality, schema validation, and observability practices to ensure trustworthy outputs\n Contribute to architecture discussions and tool evaluation, helping to shape platform scalability and governance alignment\n Write clean, maintainable, and testable code in Python and SQL, following best practices in version control, CI/CD, and documentation\n Optimize and monitor production workflows using tools such as Airflow, dbt, Athena, EMR, Kafka, and Iceberg/Parquet\n Collaborate with cross-functional stakeholders (analytics, product, compliance) to ensure technical solutions meet business needs\n Participate in on-call rotations to support critical platform jobs and reduce operational disruptions\n Champion a culture of excellence, accountability, and continuous improvement through code reviews, documentation, and knowledge sharing\n \n What you’ll need: \n \n Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field is required\n 5+ years of experience in data engineering or software development with a strong focus on data infrastructure\n Demonstrated success designing and delivering large-scale, reliable, and governed data infrastructure projects\n Track record of building and maintaining production grade systems using APIs\n Strong proficiency in Python and SQL, with experience applying software engineering rigor to data workflows (testing, version control, observability)\n Hands-on experience with modern data tools such as Airflow, dbt, AWS (EMR, Athena, S3), Iceberg, Parquet, and Kafka\n Track record of mentoring engineers and raising team-wide engineering standards through code reviews, documentation, and knowledge sharing\n Ability to work effectively in a fast-paced, agile environment, collaborating across technical and non-technical stakeholders\n Experience with healthcare data (claims, eligibility, clinical, EHR) is strongly preferred\n Understanding of data security, privacy, and compliance practices (HIPAA or similar) in pipeline and platform design\n Experience with snowflake, Databricks is a plus\n Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related field is a plus\n \n  \n Pay \u0026 Perks: \n 💻 Fully remote opportunity with about 5% travel\n 🩺 Medical, dental, vision, life, disability insurance, and Employee Assistance Program \n 📈 401K retirement plan with company match; flexible spending and health savings account \n 🏝️ Flex Time Off + company holidays\n 👶 Up to 14 weeks of paid parental leave \n 🐶 Pet insurance  \n The salary range for this position is $130,000 to $160,000 annually; as part of a total benefits package which includes health insurance, 401k and bonus. In accordance with state applicable laws, Cohere is required to provide a reasonable estimate of the compensation range for this role. Individual pay decisions are ultimately based on a number of factors, including but not limited to qualifications for the role, experience level, skillset, and internal alignment. \n  \n Interview Process*: \n \n Connect with Talent Acquisition for a Preliminary Phone Screening\n Meet your Hiring Manager!\n Feature Design\n System Design\n Behavioral Interview\n \n *Subject to change\n  \n About Cohere Health: \n Cohere Health’s clinical intelligence platform and agentic AI-powered solutions connect health plans’ strategic goals and providers’ needs, optimizing the speed, cost, and quality of care. With an enterprise approach that streamlines payer-provider decision-making across the care continuum–including policy, prior authorization, payment accuracy, and more–the company improves collaboration and reduces burden, resulting in up to 8x ROI and 94% provider s","salary_min":130000,"salary_max":160000,"location":"United States","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["healthcare","data-pipeline","cloud","payments","agents"],"apply_url":"https://job-boards.greenhouse.io/coherehealth/jobs/7978386003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-26T16:37:59Z","expires_at":"2026-09-29T13:36:35.433402Z","created_at":"2026-08-27T13:36:40.138107Z","updated_at":"2026-08-30T13:36:35.567586Z","company_name":"Cohere Health","company_slug":"cohere-health","company_logo_url":"https://www.google.com/s2/favicons?domain=coherehealth.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e9df7d6e-9243-4269-8196-cc7374752000"},{"id":"1a525cd5-6fd1-4af5-be1b-eaa2a3e7709e","company_id":"e3915539-5a8f-4461-9f26-06366a918674","title":"Senior ML Engineer, Core Development","slug":"senior-ml-engineer-core-development-94a264b5","description":"Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years.\n Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the  expertise , technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed,  built  and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a  realtime , 3D  command  and control center. As the world enters an era of strategic competition, Anduril is committed to bringing  cutting-edge  autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years.   \n About the Team:   Air Dominance \u0026 Strike designs, builds, and flies autonomous air vehicles—from  collaborative combat aircraft to expendable cruise missiles and counter-UAS  interceptors. Our vehicles move from whiteboard to first flight on timelines that  traditional primes consider impossible, which means our design cycles live or die  on how fast we can close the iteration loop. The Anduril AI Engineering team  exists to collapse that loop.    \n We are engineers first. We work from engineering first principles and unlock capability through machine learning and AI. We are building to scale across CFD, FEA, thermal, and electromagnetics, with pipelines, architectures, and validation practices that carry across programs.   \n   About the Job   We are looking for a Machine Learning Engineer to apply the latest research in  physics ML to the toughest bottlenecks in our design cycle. This role owns the  entire surrogate modeling stack for Air Dominance \u0026 Strike—the architectures,  the training infrastructure, the simulation data pipelines that feed it, and the  tooling design engineers use to consume predictions.    \n   You will develop, train, and deploy surrogate models that accelerate the physics simulations underpinning our air vehicle programs. Working alongside aerodynamicists, structures engineers, and thermal engineers, your models will directly inform decisions on hardware that actually flies. Where current methods fall short, you will develop new ones, with ample room to identify novel applications of physics ML across our portfolio.    \n   Defense experience is not required. We are looking for engineers who came to machine learning through the complex physical problems they were already trying to solve.    \n   This role is based onsite in our Costa Mesa, CA office.   \n   What You'll Do   \n \n Own the Surrogate Modeling Stack:  Drive the end-to-end design, training, and deployment of production-grade surrogate models to accelerate critical simulation workflows (CFD, FEA, thermal, structural, and aeroelastic) across air vehicle design.   \n Develop State-of-the-Art Architectures:  Design and implement neural architectures tailored to engineering physics, developing new techniques for uncertainty quantification, active learning, and inverse problems (such as geometry and shape optimization).   \n Build Robust Data \u0026 Training Infrastructure:  Create the pipelines behind the training—extracting, aggregating, and sanitizing tens of thousands of high-fidelity results from solver outputs.   \n Optimize \u0026 Integrate:  Optimize inference for the design loop (maximizing GPU utilization, batched evaluation, and interactive-speed latency) and seamlessly integrate surrogate predictions into the tooling our domain engineers already use.   \n Collaborate \u0026 Mentor:  Partner with domain engineers to identify where ML delivers the highest leverage, stay current with Physics AI research, and provide technical mentorship to non ML engineers.   \n \n Qualifications   \n \n Education:  BS, MS, or PhD in aerospace, thermal, mechanical, or electrical engineering, or in machine learning/AI/data science with a demonstrated engineering foundation.   \n Experience:  3+ years of experience taking ML models from R\u0026D into production using large-scale scientific or engineering datasets.   \n Physics ML Expertise:  Working knowledge of modern surrogate architectures (e.g. GNNs, Transolver, DoMINO \u0026 GeoTransolver) comb","salary_min":220000,"salary_max":292000,"location":"Costa Mesa, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["payments","tensorflow","distributed-systems","computer-vision","data-pipeline","pytorch","mlops","machine-learning"],"apply_url":"https://boards.greenhouse.io/andurilindustries/jobs/5216691007?gh_jid=5216691007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-26T15:59:24Z","expires_at":"2026-09-29T13:37:22.907369Z","created_at":"2026-08-27T13:37:38.482223Z","updated_at":"2026-08-30T13:37:23.045327Z","company_name":"Anduril","company_slug":"anduril","company_logo_url":"https://www.google.com/s2/favicons?domain=anduril.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/1a525cd5-6fd1-4af5-be1b-eaa2a3e7709e"},{"id":"253c13f4-0eef-4662-ba68-c99e77924251","company_id":"adc4981a-d4ff-4939-952f-362f51e1291d","title":"Sr. Manager, Security Engineering","slug":"sr-manager-security-engineering-e11dfd0b","description":"Our Mission: \n 6sense's mission is to multiply what matters: growth, retention, and efficiency.  We envision a future where companies, teams and people reach their full potential.\n Our People: \n People are the heart and soul of 6sense. We serve with passion and purpose. We live by our Being 6sense values of Win as One Team, Stay Curious, Do The Right Thing, Own the Outcome, and Create Belonging.  Every 6sensor plays a part in defining the future of our industry-leading technology.  6sense is a place where difference-makers roll up their sleeves, take risks, act with integrity, and measure success by the value we create for our customers.  We want 6sense to be the best chapter of your career. \n \n Senior Manager, Security Engineering\n Business Technology, Security \n REPORTING AREA\n Security - CISO\n FUNCTION\n Business Technology\n TEAM MODEL\n United States and India\n LEADERSHIP SCOPE\n Application/Product Security; Infrastructure/Cloud Security; Vulnerability Management\n ROLE TYPE\n Leader with technical depth\n ENVIRONMENT\n AI-first, cloud-native SaaS\n Role Purpose\n Lead the security engineering organization that protects 6sense's AI-enabled, cloud-native SaaS platform. This leader owns Vulnerability Operations, Infrastructure Security, and Application/Product Security, and is accountable for building scalable security capabilities that enable rapid product delivery without compromising customer trust, resilience, or compliance. The role leads a distributed team across the United States and India and combines strategic leadership with credible technical judgment.\n Leadership Mandate\n \n Build one integrated security engineering operating model across the three teams, with clear ownership, service expectations, priorities, and measurable outcomes.\n Partner with Product, Engineering, Cloud Infrastructure, Data, AI/ML, Security Operations, Privacy, GRC, and Enterprise Technology leaders to embed security into planning and delivery.\n Create an inclusive, high-accountability culture across time zones using clear decisions, durable documentation, effective handoffs, and intentional overlap for critical work.\n Balance hands-on technical engagement with people leadership, program ownership, stakeholder influence, and executive-level risk communication.\n \n Core Responsibilities\n 1. Organization and People Leadership\n \n Lead, coach, and develop managers and engineers across the United States and India. Establish role clarity, career paths, succession coverage, and consistent performance expectations.\n Create an operating cadence that supports asynchronous execution, reliable cross-region handoffs, rapid escalation, and shared accountability.\n Build workforce and capacity plans aligned to product growth, AI investment, risk, and business priorities.\n Foster a culture of constructive challenge, disagree and commit, continuous learning, quality, and automation-first improvement.\n \n 2. AI and Product Security\n \n Own the security strategy for AI-enabled product capabilities from design through production, including threat modeling, architecture review, secure development standards, testing, monitoring, and release readiness.\n Address AI-specific risks such as prompt injection, insecure tool or agent access, sensitive-data exposure, model and data pipeline integrity, excessive agency, abuse, and third-party model or service dependencies.\n Partner with AI/ML, Product, and Engineering teams to define secure patterns for models, agents, retrieval-augmented generation, application programming interfaces, data access, and human approval controls.\n Advance product security practices including secure software development lifecycle controls, code and design review, application security testing, penetration testing, security champions, and coordinated vulnerability disclosure or bug bounty.\n \n 3. Vulnerability Operations\n \n Own end-to-end vulnerability discovery, prioritization, remediation governance, exception management, and validation across applications, cloud infrastructure, containers, endpoints, operating systems, and third-party components.\n Move beyond severity-only prioritization by incorporating exploitability, internet exposure, asset criticality, data sensitivity, available compensating controls, and active threat intelligence.\n Improve remediation speed and predictability through automation, clear service-level objectives, transparent ownership, and decision-ready reporting.\n Establish effective coverage for software supply chain risk, including open-source dependencies, build systems, artifacts, secrets, and continuous integration and delivery pipelines.\n \n 4. Infrastructure and Cloud Security\n \n Own preventive and detective security guardrails for the AWS environment, infrastructure as code, containers, identity and access, network boundaries, workloads, secrets, logging, and data services.\n Partner with Infrastructure and Platform Engineering to make secure cloud patterns easy to adopt and to reduce reliance on man","salary_min":204721,"salary_max":254258,"location":"New York, NY","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["llm","data-pipeline","cloud","agents","alignment","rag","security"],"apply_url":"https://boards.greenhouse.io/6sense/jobs/8139157?gh_jid=8139157","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-26T13:37:13Z","expires_at":"2026-09-29T13:41:05.139986Z","created_at":"2026-08-26T13:41:05.372167Z","updated_at":"2026-08-30T13:41:05.275021Z","company_name":"6sense","company_slug":"6sense","company_logo_url":"https://www.google.com/s2/favicons?domain=6sense.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/253c13f4-0eef-4662-ba68-c99e77924251"},{"id":"479ee1e5-bda5-4253-9664-0645a438ac86","company_id":"5d6de1f6-4d6c-463b-8a2b-a5caeadb97b4","title":"Senior Software Engineer - Airflow Infrastructure, NYC","slug":"senior-software-engineer-airflow-infrastructure-nyc-7ce717b7","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 Senior 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. Your 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\nHybrid Work Model: For this role, you will embrace a flexible hybrid work model with at least 3 days per week in our New York City office.\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 - 5+ years of experience building and delivering SaaS products.\n\n - Strong proficiency in Python or Golang.\n\n - Hands-on experience with 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\n\nThe estimated salary for this role ranges from $210,000 - $250,000 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":210000,"salary_max":250000,"location":"New York, NY","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["cloud","data-pipeline","distributed-systems","infrastructure"],"apply_url":"https://jobs.ashbyhq.com/astronomer/c02288d2-e50a-4ef0-8151-c5d3f6c333af/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-26T08:14:31.454Z","expires_at":"2026-09-29T13:47:15.068812Z","created_at":"2026-08-26T13:47:11.32077Z","updated_at":"2026-08-30T13:47:15.197828Z","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/479ee1e5-bda5-4253-9664-0645a438ac86"},{"id":"82dec910-1062-4275-8ca4-cb2a591a1dd1","company_id":"5d6de1f6-4d6c-463b-8a2b-a5caeadb97b4","title":"Senior Software Engineer - Build, NYC","slug":"senior-software-engineer-build-nyc-e267b790","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\n\n\nABOUT THIS ROLE\n\nApache Airflow is one of the most popular open-source data platform tools. It powers the data platforms at nearly every large company and fast-growing startups: Airbnb, Uber, OpenAI, Anthropic, Nike, Capital One, Disney all use Airflow extensively. At Astronomer, we’re the largest contributors to the project and are building commercial products around Airflow to make it easier to use, run, and scale.\n\n\n\nWe’re in a unique position: as the company behind Apache Airflow, we see data as it moves across entire organizations—from raw ingestion to production dashboards, machine learning models, and AI products. Leveraging this vantage point, our R\u0026D team is developing a global context layer for data—an intelligence layer that powers search and discovery, code generation for data and analytics, and automated root cause analysis. LLMs are already quite good at writing Python and SQL code against data platforms; we think this context layer will give them the metadata necessary for data practitioners everywhere to use LLMs effectively.\n\n\n\nAs a Software Engineer on this team, you’ll help design and build this foundation and the applications around it. You’ll work on some of the hardest and most exciting challenges in data—search, information retrieval, and AI for data pracitioners—while collaborating with a small, highly skilled team that values velocity, creativity, and impact. This role sits at the intersection of applied research, software engineering, and product: we think it takes someone who can work across the stack to build, release, and scale products successfully in this space.\n\n\n\nHybrid Work Model: For this role, you will embrace a flexible hybrid work model with at least 3 days per week in our New York City office.\n\n\n\n\n\nWHAT YOU GET TO DO:\n\n - Shape the future of AI for data engineering - build intelligent systems that understand, reason about, and optimize the flow of data across entire organizations.\n\n - Design and engineer the brain of Astronomer’s context layer, crafting components that power data modeling, semantic search, retrieval, and code generation.\n\n - Push the boundaries of applied AI - experiment with LLMs, embeddings, and cutting-edge retrieval techniques to create developer tools that deliver insights to you and our customers.\n\n - Turn research into reality - work side by side with R\u0026D and product teams to bring early AI concepts to life in the product experience.\n\n - Solve high-impact information retrieval and search challenges at a global scale, leveraging Astronomer’s unparalleled visibility into data pipelines across industries.\n\n - Influence the technical vision and architecture for the next generation of AI-driven data products.\n\n - Represent Astronomer in the community - through open-source contributions, technical talks, and publications that showcase our leadership in AI and data innovation.\n\n\n\n\nWHAT YOU BRING TO THE ROLE:\n\n - 5-8 years of software engineering experience with Python or Go\n\n - Empathy for users, and a deep interest in improving the workflows of data professionals.\n\n - Familiarity with early-stage product development; comfortable working with ambiguity in a fast-changing field.\n\n - Experience with LLMs, vector databases, embeddings, or other applied AI areas—or a strong desire to dive in.\n\n - A creative, experimental mindset: you enjoy exploring uncharted areas, validating hypotheses, and learning through iteration.\n\n - Strong collaboration and communication skills—you can explain complex systems clearly to both technical and non-technical audiences.\n\n - A collaborative approach and comfort working in an evolving, research-driven environment where ideas move quickly.\n\n\n\n\nBONUS POINTS IF YOU HAVE:\n\n - A passion for AI systems for data, developer tools, or machine learning infrastructure.\n\n - Familiarity with Apache Airflow or other orchestration tools.\n\n - Demonstrated contributions to open source projects.\n\n - Experience in search, IR, or large-scale data infrastructure.\n\n - Exposure to early-stage startups or R\u0026D organizations where ambiguity is the norm.\n\n - Experience building out agentic systems on top of frontier models.\n\n\n\nThe estimated total compensation for this role ranges from $210,000 - $250,000 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 qualific","salary_min":210000,"salary_max":250000,"location":"New York, NY","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["data-pipeline","code-generation","llm","search","embeddings","agents"],"apply_url":"https://jobs.ashbyhq.com/astronomer/3c72cd98-3493-4d30-8897-dc5e836db67e/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-26T08:13:00.068Z","expires_at":"2026-09-29T13:47:14.977036Z","created_at":"2026-08-26T13:47:11.23022Z","updated_at":"2026-08-30T13:47:15.105522Z","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/82dec910-1062-4275-8ca4-cb2a591a1dd1"},{"id":"44046e1e-69ad-4692-9110-7bb87edaadaf","company_id":"43dd17db-bec3-4ebb-a432-f71d57a9aa47","title":"Staff Software Engineer, Imaging","slug":"staff-software-engineer-imaging-4ba96095","description":"THE OPPORTUNITY\n\ninsitro is a physical AI company dedicated to unlocking causal human biology and accelerating the delivery of better medicines to patients. Our unique Virtual Human™ platform identifies novel, high-impact genetic intervention points, which our TherML™ platform translates into therapeutics—whether small molecules, biologics, or oligos. With multiple programs in metabolic disease and neuroscience advancing toward the clinic, and our first IND submission slated for the second half of this year, we are at a pivotal inflection point.\n\nAs a Staff Software Engineer on our Imaging Software team, you will define and expand our computer vision and ML infrastructure across the full imaging data lifecycle — from on-microscope acquisition to high-throughput ML pipelines. You'll build the platform features that make novel imaging modalities and ML-derived phenotypes integral to our discovery workflows, partnering daily with lab scientists, ML scientists, and our microscopy team to turn research prototypes into validated screening workflows that run reliably at laboratory automation scale. This is a chance to set the technical direction for how imaging, automation, and machine learning converge in drug discovery.\n\nBased in South San Francisco, this position reports directly to the Director of Imaging, Cellular Machine Learning and offers an in-person hybrid schedule of three days per week.\n\n\n\n\nRESPONSIBILITIES\n\nPlatform \u0026 Tooling\n\n• Platform Enablement: Partner with lab and ML scientists to design, develop, and scale the platform capabilities needed to run and interpret ML-powered high-content imaging screens\n\n• User Tooling: Build and evolve robust tools and interactive interfaces for data exploration, quality assessment, and visualization so scientists can iterate quickly on experimental data\n\n• Architectural Ownership: Own complex, end-to-end projects, making thoughtful architectural trade-offs, and delivering incrementally with long-term maintainability in mind\n\nProduction Hardening \u0026 Data Integrity\n\n• Production Hardening: Scale and harden complex image processing and ML workflows, taking them from research prototypes to systems that reliably process millions of images per day\n\n• Data Integrity: Set and uphold best-in-class practices for data integrity, lineage tracking, reproducibility, and observability across the entire imaging data lifecycle\n\n• Documentation: Write clear, exemplary technical specifications and documentation that others build on\n\nCross-Functional Partnership\n\n• Scientific Translation: Work closely with lab scientists, ML scientists, and microscopy teams to translate complex experimental needs into clear, actionable technical plans and shipped software\n\n• Mentorship: Raise the technical bar across the team by sharing knowledge and mentoring other engineers\n\n \n\n\nABOUT YOU\n\nExperience \u0026 Qualifications\n\n• Proven Tenure: 8+ years of professional experience building and operating production-grade software and high-throughput data pipelines, primarily in Python\n\n• ML Platform Depth: You have designed, built, and deployed scientific computing pipelines, visualizations, and QC processes for large-scale imaging or similarly high-dimensional datasets\n\n• Distributed Systems Stack: Hands-on experience with a Python-first ML stack, distributed compute (e.g., PyTorch/Lightning, Ray, Kubernetes), and workflow orchestration (e.g., Argo, Airflow, or redun)\n\n• End-to-End Delivery: A track record of owning complex systems from architecture through production operation\n\nCore Competencies\n\n• Cross-Functional Partnership: You thrive alongside scientists and excel at translating abstract research needs into practical, scalable software\n\n• Mission-Driven: You're motivated by enabling scientific breakthroughs through robust platform engineering\n\n• Force Multiplier: You enjoy mentoring and leveling up the engineers around you \n\n\n\n\nCOMPENSATION \u0026 BENEFITS AT INSITRO\n\nOur target starting salary for successful US-based applicants for this role is $219,000 - $233,000. To determine starting pay, we consider multiple job-related factors including a candidate's skills, education and experience, market demand, business needs, and internal parity. We may also adjust this range in the future based on market data.\n\nThis role is eligible for participation in our Annual Performance Bonus Plan (based on company targets by role level and annual company performance) and our Equity Incentive Plan, subject to the terms of those plans and associated policies.\n\n\n\nIn addition, insitro also provides our employees:\n\n - 401(k) plan with employer matching for contributions\n\n - Excellent medical, dental, and vision coverage as well as mental health and well-being support\n\n - Open, flexible vacation policy\n\n - Paid parental leave of at least 16 weeks to support parents who give birth, and 10 weeks for a new parent (inclusive of birth, adoption, fostering, etc)\n\n - Quarterly budget for books and","salary_min":219000,"salary_max":233000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["healthcare","payments","data-pipeline","fine-tuning","pytorch","computer-vision","distributed-systems"],"apply_url":"https://jobs.ashbyhq.com/insitro/ff6605ae-4961-4a06-b656-6d7dc665d990/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-25T22:43:18.642Z","expires_at":"2026-09-29T13:36:50.099247Z","created_at":"2026-08-26T13:36:56.743297Z","updated_at":"2026-08-30T13:36:50.234419Z","company_name":"Insitro","company_slug":"insitro","company_logo_url":"https://www.google.com/s2/favicons?domain=insitro.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/44046e1e-69ad-4692-9110-7bb87edaadaf"},{"id":"a85f6a22-2c3d-4d63-bc92-fb6bab844254","company_id":"72014eb6-e84d-48c2-af5c-5424ebec0b3c","title":"Senior Staff Machine Learning Systems Engineer, Ads ML Platform","slug":"senior-staff-machine-learning-systems-engineer-ads-ml-platform-555ec238","description":"Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com .\n Reddit has a flexible workforce! If you happen to live close to one of our physical office locations our doors are open for you to come into the office as often as you'd like. Don't live near one of our offices? No worries: You can apply to work remotely in any country in which we have a physical presence\n About Reddit Reddit is a community of communities, built on shared interests, passion, and trust. With 100,000+ active communities and 101M+ daily active unique visitors, we’re one of the largest sources of conversation and knowledge on the internet. For more information, visit redditinc.com .\n Team Overview \n The Ads ML Platform team builds infrastructure that accelerates high-scale ML systems and tooling for Ads ML, while extending reusable capabilities to broader Reddit ML use cases where appropriate. Our systems help ML engineers move faster across the full development lifecycle: creating features, generating training data, running offline experiments, validating model quality, launching production models, and operating ML systems reliably.\n We are looking for a Senior Staff Machine Learning Systems Engineer to lead the technical strategy for the end-to-end Ads ML engineer lifecycle. The initial focus will be on the feature development and training iteration loop: making it faster and easier for ML engineers to build features, generate reliable training data, run experiments, and move from idea to validated model improvement. Over time, this scope will expand into serving and online experimentation workflows, creating a more seamless path from offline iteration to production impact.\n This is a senior technical leadership role for someone who can combine deep systems expertise, production ML experience, architectural judgment, and cross-team influence.\n What You’ll Do \n \n Own the technical strategy for the end-to-end Ads ML engineer lifecycle, starting with feature development, training data, offline experimentation, and model iteration workflows.\n Align Ads ML platform priorities with Reddit’s broader ML Platform vision, translating Ads pain points into reusable platform capabilities where appropriate.\n Define architecture and technical standards for ML feature and training-data systems across batch/streaming computation, backfills, lineage, quality, observability, and online/offline consistency.\n Stay close to ML engineers and platform customers to identify high-leverage friction points and improve day-to-day development velocity.\n Build platform abstractions and workflow automation that make ML development faster, safer, more reliable, and more self-service.\n Over time, extend the platform strategy into serving and online experimentation workflows, creating a more seamless offline-to-online ML development experience.\n Partner across Ads, ML Platform, Data Platform, modeling, product, and engineering teams to clarify ownership, resolve ambiguity, and drive durable execution.\n Mentor Staff and senior engineers, raise the architecture and operational bar, and help grow the next generation of technical leaders.\n \n Who You Might Be \n \n You have 8+ years of experience in infrastructure, distributed systems, ML platforms, data platforms, or large-scale backend systems.\n You have 4+ years building or operating production ML infrastructure, feature platforms, training data systems, experimentation systems, or large-scale data pipelines.\n You have led broad, ambiguous, multi-team platform initiatives from strategy through adoption.\n You have built platforms used directly by ML engineers, data scientists, or product teams developing production ML systems.\n You have deep experience in ML platform, feature platform, training data, experimentation, developer infrastructure, or distributed data infrastructure.\n You have worked with distributed data and compute systems such as Spark, Flink, Kafka, Ray, Airflow, Iceberg, Kubernetes, BigQuery, Snowflake, Databricks, or similar technologies.\n You can balance urgent customer needs with durable long-term architecture and reusable platform patterns.\n You influence senior engineers and leaders through clear technical reasoning, RFCs, design reviews, decision frameworks, and operating mechanisms.\n You are excited to shape how production ML systems are built, scaled, and operated, not only how models are trained.\n \n Benefits: \n \n Comprehensive Healthcare Benefits and Income Replacement Programs\n 401k with Employer Match\n Global Benefit programs that fit your lifestyle, from workspace to professional development to ca","salary_min":292500,"salary_max":409500,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"lead","tags":["healthcare","distributed-systems","data-pipeline","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/reddit/jobs/8157275","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-25T18:40:31Z","expires_at":"2026-09-29T13:38:59.415338Z","created_at":"2026-08-25T19:40:19.020618Z","updated_at":"2026-08-30T13:38:59.553319Z","company_name":"Reddit","company_slug":"reddit","company_logo_url":"https://www.google.com/s2/favicons?domain=www.reddit.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a85f6a22-2c3d-4d63-bc92-fb6bab844254"},{"id":"2582184d-bb17-483b-ba2f-bb2282d9c08b","company_id":"e3915539-5a8f-4461-9f26-06366a918674","title":"Field Reliability Engineer - Undersea Reconnaissance \u0026 Strike","slug":"field-reliability-engineer-undersea-reconnaissance-strike-0b776e4a","description":"Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years.\n Anduril Maritime builds autonomous underwater/surface vehicles: autonomy computers running control and mission logic, payload computers driving sonar and other sensors, surface integration units, and ground control stations. We're past the \"does it work in the lab\" phase - vehicles are in the field, and field vehicles find bugs the lab never does. We need someone whose job, starting day one, is to pick up those bugs and actually close them: reproduce it, find the real cause, fix it, ship it, tell the team what happened.\n To be clear about what this isn't: it's not a ticket-triage-and-forward job, and it's not \"junior dev does the boring work while senior devs build features.\" You'll be shipping real fixes in the real codebase. You're just entering the codebase through \"this broke on a vehicle\" instead of \"here's a clean feature spec.\" Once you've built up cross-subsystem fluency - which this role forces faster than sitting in one subsystem ever would - you move into owning subsystems and roadmap work like anyone else.\n What you'll actually be doing \n \n Take a field-reported anomaly (sensor stopped publishing, mission faulted mid-track, data stream drifted for no obvious reason) and figure out what subsystem it's actually in - not just where it was reported.\n Reproduce it for real. Replay recorded sensor/mission data through the live pipeline, pull it into visualization tooling, don't guess.\n Use the metrics and log infrastructure that already exists (per-service metrics, dashboards, centralized logs) to reconstruct system state at the moment things went wrong, instead of re-running until you get lucky.\n Confirm the fix in a simulated environment first, hardware-in-the-loop where that matters, before it goes anywhere near a real vehicle.\n Write the fix in whatever language the subsystem actually uses - Rust, C++, Python, Go, whatever — with tests, through CI, out through the normal versioned rollout. No special \"triage\" shortcut path.\n Figure out whether the actual problem is a bug, a bad config, or a deployment mismatch, and fix it at the layer where it actually lives instead of patching around it one level up.\n Write down root cause and fix for every issue you close. Not for the paperwork — so we can see, over months, which subsystems keep breaking and which tests we're missing. Help build the on-call/triage runbook this team doesn't have yet.\n Push back to the owning team when the real fix is \"this subsystem needs to change,\" not \"patch the symptom and move on.\"\n \n  What you need to already have \n \n Real experience debugging code you didn't write, under time pressure, with incomplete information. This matters more than which language you know best.\n Enough comfort in at least two of Rust, modern C++, Python, Go to read and fix bugs in them. You don't need to be an expert in all four - you need to not freeze up when you're dropped into one you know less well.\n Systems-debugging instincts: pull logs and metrics, reconstruct what happened, reproduce offline from recorded data instead of only live.\n Some real exposure to observability tooling (Prometheus/Grafana-class metrics, Loki/ELK-class logs) — you should already know how to use this stuff, not learn it here.\n Linux and systems fluency. Bonus if you've touched declarative/immutable OS setups (NixOS-style).\n The ability to write up what actually happened clearly, without padding it out or burying the cause. \"The vehicle did something weird\" is not an acceptable final answer from you.\n Willingness to work close to hardware and sensors even if you've never touched maritime or robotics before. We're not looking for domain background, we're looking for the debugging instinct.\n Willingness to travel 25%+ of the time on average\n \n Nice to have, not required \n \n Robotics middleware, sensor drivers, or real-time data pipeline experience (sonar, radar, lidar, whatever).\n Time in simulation-in-the-loop or hardware-in-the-loop test setups.\n CI/CD and versioned deployment experience for embedded or fleet software.\n Actual on-call or field-support experience for hardware that ships to real users, not just internal tools.\n \n Where this goes \n \n This is a real entry point, not a holding pen. Do this well and you'll know more of the stack than most ","salary_min":166000,"salary_max":220000,"location":"Quincy, MA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["payments","computer-vision","cloud","data-pipeline","robotics"],"apply_url":"https://boards.greenhouse.io/andurilindustries/jobs/5221278007?gh_jid=5221278007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-25T15:01:43Z","expires_at":"2026-09-29T13:37:14.216413Z","created_at":"2026-08-25T18:28:18.875116Z","updated_at":"2026-08-30T13:37:14.352331Z","company_name":"Anduril","company_slug":"anduril","company_logo_url":"https://www.google.com/s2/favicons?domain=anduril.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/2582184d-bb17-483b-ba2f-bb2282d9c08b"},{"id":"566091c6-3c18-4106-9957-b985f48485eb","company_id":"e3915539-5a8f-4461-9f26-06366a918674","title":"Mission Software Engineer - Undersea Reconnaissance \u0026 Strike","slug":"mission-software-engineer-undersea-reconnaissance-strike-c9f12e25","description":"Anduril Industries is a defense technology company with a mission to transform U.S. and allied military capabilities with advanced technology. By bringing the expertise, technology, and business model of the 21st century’s most innovative companies to the defense industry, Anduril is changing how military systems are designed, built and sold. Anduril’s family of systems is powered by Lattice OS, an AI-powered operating system that turns thousands of data streams into a realtime, 3D command and control center. As the world enters an era of strategic competition, Anduril is committed to bringing cutting-edge autonomy, AI, computer vision, sensor fusion, and networking technology to the military in months, not years.\n Anduril Maritime builds autonomous underwater/surface vehicles: autonomy computers running control and mission logic, payload computers driving sonar and other sensors, surface integration units, and ground control stations. We're past the \"does it work in the lab\" phase - vehicles are in the field, and field vehicles find bugs the lab never does. We need someone whose job, starting day one, is to pick up those bugs and actually close them: reproduce it, find the real cause, fix it, ship it, tell the team what happened.\n To be clear about what this isn't: it's not a ticket-triage-and-forward job, and it's not \"junior dev does the boring work while senior devs build features.\" You'll be shipping real fixes in the real codebase. You're just entering the codebase through \"this broke on a vehicle\" instead of \"here's a clean feature spec.\" Once you've built up cross-subsystem fluency - which this role forces faster than sitting in one subsystem ever would - you move into owning subsystems and roadmap work like anyone else.\n What you'll actually be doing \n \n Take a field-reported anomaly (sensor stopped publishing, mission faulted mid-track, data stream drifted for no obvious reason) and figure out what subsystem it's actually in - not just where it was reported.\n Reproduce it for real. Replay recorded sensor/mission data through the live pipeline, pull it into visualization tooling, don't guess.\n Use the metrics and log infrastructure that already exists (per-service metrics, dashboards, centralized logs) to reconstruct system state at the moment things went wrong, instead of re-running until you get lucky.\n Confirm the fix in a simulated environment first, hardware-in-the-loop where that matters, before it goes anywhere near a real vehicle.\n Write the fix in whatever language the subsystem actually uses - Rust, C++, Python, Go, whatever — with tests, through CI, out through the normal versioned rollout. No special \"triage\" shortcut path.\n Figure out whether the actual problem is a bug, a bad config, or a deployment mismatch, and fix it at the layer where it actually lives instead of patching around it one level up.\n Write down root cause and fix for every issue you close. Not for the paperwork — so we can see, over months, which subsystems keep breaking and which tests we're missing. Help build the on-call/triage runbook this team doesn't have yet.\n Push back to the owning team when the real fix is \"this subsystem needs to change,\" not \"patch the symptom and move on.\"\n \n  What you need to already have \n \n Real experience debugging code you didn't write, under time pressure, with incomplete information. This matters more than which language you know best.\n Enough comfort in at least two of Rust, modern C++, Python, Go to read and fix bugs in them. You don't need to be an expert in all four - you need to not freeze up when you're dropped into one you know less well.\n Systems-debugging instincts: pull logs and metrics, reconstruct what happened, reproduce offline from recorded data instead of only live.\n Some real exposure to observability tooling (Prometheus/Grafana-class metrics, Loki/ELK-class logs) — you should already know how to use this stuff, not learn it here.\n Linux and systems fluency. Bonus if you've touched declarative/immutable OS setups (NixOS-style).\n The ability to write up what actually happened clearly, without padding it out or burying the cause. \"The vehicle did something weird\" is not an acceptable final answer from you.\n Willingness to work close to hardware and sensors even if you've never touched maritime or robotics before. We're not looking for domain background, we're looking for the debugging instinct.\n Willingness to travel 25%+ of the time on average\n \n Nice to have, not required \n \n Robotics middleware, sensor drivers, or real-time data pipeline experience (sonar, radar, lidar, whatever).\n Time in simulation-in-the-loop or hardware-in-the-loop test setups.\n CI/CD and versioned deployment experience for embedded or fleet software.\n Actual on-call or field-support experience for hardware that ships to real users, not just internal tools.\n \n Where this goes \n \n This is a real entry point, not a holding pen. Do this well and you'll know more of the stack than most ","salary_min":166000,"salary_max":220000,"location":"Quincy, MA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["robotics","payments","cloud","computer-vision","data-pipeline"],"apply_url":"https://boards.greenhouse.io/andurilindustries/jobs/5221253007?gh_jid=5221253007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-25T15:01:09Z","expires_at":"2026-09-29T13:37:17.044808Z","created_at":"2026-08-25T18:28:19.025403Z","updated_at":"2026-08-30T13:37:17.180322Z","company_name":"Anduril","company_slug":"anduril","company_logo_url":"https://www.google.com/s2/favicons?domain=anduril.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/566091c6-3c18-4106-9957-b985f48485eb"}],"page":1,"per_page":20,"total":1098,"total_is_exact":true,"total_pages":55}