{"access":{"catalog_url":"https://aidevboard.com/api/v1/catalog","description":"Public read endpoints are open and free. 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Every day, we bring creativity, experimentation, and advanced AI to reshape access to credit, helping millions move forward financially with clarity and confidence.\n As the leading AI lending marketplace, we partner with banks and credit unions to expand access to affordable credit through technology that’s both radically intelligent and deeply human. Our platform runs over one million predictions per borrower using more than 3,000 signals, powering smarter, fairer decisions for millions of customers. But the numbers only hint at the impact. Every idea, every voice, and every contribution moves us closer to a world where credit never stands between people and their financial progress.\n We’re proudly digital-first, giving most Upstarters the flexibility to do their best work from wherever they thrive, alongside teammates across 80+ cities in the US and Canada. Digital-first doesn’t mean distant. We’re intentional about in-person connection through team onsites, planning sessions, and moments that spark creativity and trust. And whether you choose to work primarily from home or collaborate in-person from one of our offices in Columbus, Austin, the Bay Area, or New York City (opening Summer 2026), you’ll have the support to work in the way that works best for you.\n If you’re energized by tackling meaningful problems, excited to innovate with purpose, and motivated by work that truly matters, we’d love to hear from you.\n The Team:  \n The Machine Learning and Simulations Platform (MLSP) team builds and operates the core infrastructure that powers ML model training,feature engineering,  inference, and marketplace simulation at Upstart. Every underwriting, fraud, conversion, and verification model runs on this platform. We own the full production path: the data and features that feed a model, the infrastructure that serves it at decision time, the tooling that deploys it, and the simulation systems that predict business impact before a change goes live.\n We are reimagining that platform to keep pace with our ML teams. That work spans low latency and GPU model serving, self-service model deployment, a feature platform that gives ML one place to define and serve production features, and high fidelity marketplace simulation. The team partners closely with ML, Engineering, Product, Data Platform.\n As a Senior Software Engineer on the ML and Simulations Platform team at Upstart, you will be responsible for building an MLOps platform to support machine learning model inference, process automation, model deployment, and observability. Machine Learning is critical to Upstart’s core business, and our greatest competitive advantage lies in the fact that we’re able to innovate on our AI engine quickly. You will also help build a  marketplace simulation platform to support rapid innovation across ML and Finance teams.\n How you’ll make an impact\n \n Build, maintain, and optimize  Upstart’s next-generation machine learning and simulation platform, enabling increased scale, performance, and confidence in decisioning.\n Develop  high-quality software applications that enable machine learning models to be applied to the ever-evolving needs of the business\n Build self-service tooling so ML teams can register features and deploy models independently, and reduce the manual work the platform team absorbs today.\n Deliver the data and feature infrastructure behind every model, including feature definition, storage, serving, and offline to online parity.\n Design and contribute to  our simulation systems to more accurately reflect production environments, reducing simulation cost and enabling broader usage across teams.\n Communicate closely with cross-functional partners from  ML, Engineering, Product, and Data Engineering  teams, keeping all stakeholders informed\n Mentor engineers across the team, sharing expertise on distributed systems,MLOps,  and scalable architecture.\n \n Minimum Qualifications  \n \n 6+ years of software engineering experience.\n Experience building and maintaining backend software services and APIs.\n Experience with distributed systems or large scale data processing, using Spark, Databricks, Ray, or an equivalent.\n Experience with an ML platform or the ML production path, such as training pipelines, model serving, feature pipelines, or a training data platform.\n Proficiency with some or many of the following: Python, Kotlin, Databricks, and AWS.\n Exhibits a growth mindset. You pick up new technologies that fit the task, and you learn from others.\n Ability to quickly comprehend complex requirements from ML, product, or engineering leadership, and translate them for both technical and non-technical partners.\n \n Preferred Qualifications \n \n Skill with Metaflow, MLflow, gRPC, Spark/PySpark, dbt, Ray, GPU\n Knowledge of simulation, experimentation, or ","salary_min":166900,"salary_max":230000,"location":"United States","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["api-design","mlops","distributed-systems","platform","machine-learning"],"apply_url":"https://careers.upstart.com/jobs?gh_jid=8161883","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-27T16:22:50Z","expires_at":"2026-09-29T13:45:21.136887Z","created_at":"2026-08-29T13:46:50.095597Z","updated_at":"2026-08-30T13:45:21.267566Z","company_name":"Upstart","company_slug":"upstart","company_logo_url":"https://www.google.com/s2/favicons?domain=upstart.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f14f7e06-beb3-4c13-ab57-c9011d97da6e"},{"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":"d950a0af-81cc-4c4a-b562-707b97ae1ebb","company_id":"fe4d898e-86f1-400e-95db-988d7f632620","title":"Senior Director, Data","slug":"senior-director-data-460239ea","description":"Position Overview \n As our Senior Director of Data, you will serve as the strategic visionary and executive engine powering our company’s data transformation. In this high-impact leadership role, you will redefine how we leverage information by driving enterprise data strategy, pioneering AI data readiness, commanding end-to-end pipeline engineering, and unlocking competitive advantages through cutting-edge predictive analytics. You will champion, build, and inspire a world-class team across data engineering, analytics, and data science, scaling modern infrastructure, deploying production-grade ML models, and relentlessly embedding a fearless, evidence-based, data-driven culture across every level of the organization. \n Key Responsibilities \n 1. Strategy \u0026 Organizational Data Leadership \n \n \n \n Define and execute the enterprise data, AI data readiness, and analytics strategy aligned with business objectives. \n Champion a data-driven culture across departments by elevating data literacy and self-service analytics. \n Build, mentor, and lead high-performing teams of data engineers, data scientists, and analysts. \n \n \n 2. AI Readiness \u0026 Data Science Leadership \n \n \n \n Drive the AI data strategy, ensuring data is curated, labeled, and optimized for ML/AI model development and deployment. \n Oversee the end-to-end lifecycle of machine learning models, predictive analytics, and feature store infrastructure. \n Collaborate with business partners to identify high-impact AI/ML opportunities that drive strategic value. \n \n \n 3. Pipeline Engineering \u0026 Infrastructure \n \n \n \n Oversee modern data engineering, architectural design, and reliable ETL/ELT pipelines for batch and real-time processing. \n Architect scalable data warehouses, data lakes, and modern data stack operations (MLOps). \n \n \n 4. Governance, Analytics \u0026 Reporting \n \n \n \n Establish governance policies to ensure high data quality, security, and global regulatory compliance (e.g., GDPR, CCPA). \n Deliver key executive dashboards, visualizations, and A/B testing frameworks to measure operational KPIs. \n \n \n Qualifications \n \n Experience: 10+ years of progressive leadership experience across data engineering, analytics, and data science. \n Leadership: Proven track record of managing data engineering and ML teams while fostering a data-driven organizational culture. \n Technical Mastery: Strong hands-on knowledge of Python, SQL, modern ETL tools, cloud data warehouses, and major ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn). \n Architecture \u0026 Pipelines: Deep expertise in pipeline design, streaming data architectures, MLOps, and feature store management. \n Education: Master's or Ph.D. in Data Science, Computer Science, Statistics, or related quantitative field. \n Communication: Excellent capability to translate complex technical concepts into clear strategic insights for non-technical stakeholders. \n The posted pay range represents the anticipated low and high end of the compensation for this position and is subject to change based on business need. To determine a successful candidate’s starting pay, we carefully consider a variety of factors, including primary work location, an evaluation of the candidate’s skills and experience, market demands, and internal parity. For roles with on-target-earnings (OTE), the pay range includes both base salary and target incentive compensation. Target incentive compensation for some roles may include a ramping draw period. Compensation is higher for those who exceed targets. Candidates may receive more information from the recruiter.\n Pay Range\n $194,400 — $432,000 USD \n  \n Navan uses AI-assisted Automated Employment Decision Tool (Metaview) to assist with evaluating resumes against job qualifications for this role. All final decisions are made by human recruiters and hiring managers. \n Human oversight:   Metaview does not automatically reject candidates or make final hiring decisions. Our recruiters and hiring managers review all outputs and make the final hiring decision regarding every application.  \n \n \n Your rights: If you prefer to have your application reviewed without AI assistance, you may request a human evaluation by entering your email here . Your decision to do so will not affect how your candidacy is evaluated. \n \n Please refer to our Candidate Privacy Notice for more information about our processing of personal data, and your rights.","salary_min":194400,"salary_max":432000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["pytorch","tensorflow","mlops","data-pipeline"],"apply_url":"https://navan.com/careers/openings?gh_jid=8145890","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-21T16:58:39Z","expires_at":"2026-09-29T13:48:42.690923Z","created_at":"2026-08-25T18:33:21.168009Z","updated_at":"2026-08-30T13:48:42.820522Z","company_name":"Navan","company_slug":"navan","company_logo_url":"https://www.google.com/s2/favicons?domain=navan.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/d950a0af-81cc-4c4a-b562-707b97ae1ebb"},{"id":"3707734c-f7ec-4eee-bd06-3bd04c3d3355","company_id":"52f44519-9f93-4eac-ae0b-8be13e385ebe","title":"Cloud DevOps Engineer","slug":"cloud-devops-engineer-c42c2a76","description":"CLOUD DEVOPS ENGINEER\n\n\n\nYou'll build the cloud infrastructure that turns the open web into data — the platform beneath Firecrawl's crawling, scraping, and search products. We need engineers who can make that foundation — Kubernetes, storage, networking, deployments — fast, reliable, and cheap at web scale. You'll own real infrastructure from day one — not tickets in a backlog.\n\n \n\nSalary Range: $246,000–$271,000/year\n\nEquity Range: Competitive equity — details shared during the process.\n\nLocation: San Francisco, CA (Onsite)\n\nJob Type: Full-Time \n\nExperience: 5+ years in DevOps, Platform Engineering or Cloud Infrastructure \n\nVisa: Must be legally authorized to work in the United States. We're not able to sponsor visas right now, though that may change down the line.\n\n\n\n\nABOUT FIRECRAWL\n\nFirecrawl is the easiest way to turn the web into data AI agents can use. One API call converts any URL into clean, LLM-ready markdown or structured data - the boring-hard problem everyone building with LLMs eventually hits, solved.\n\nWe hit 8 figures in ARR in year one and more than doubled it in year two. We have 170k+ GitHub stars, and developers, agents, and category-defining AI companies build on us every day. Growth like this is rare, and we're just getting started.\n\nWe're a small team punching far above our weight. Everyone here owns a real piece of the product and company, end to end, and runs it themselves - no hiding behind process or headcount.\n\nThis is a place for people who want to work at the frontier: an AI company building the infrastructure other AI companies run on, not one bolting AI onto an existing product. We move fast, go deep, and are building the tools superintelligence will rely on to gather data from the web.\n\n\n\n\nWHAT YOU'LL DO\n\n - Design, build, and operate GCP and on-prem infrastructure behind Firecrawl's products\n\n - Run large stateful and high-throughput workloads on Kubernetes — search clusters, crawling fleets, queues, and databases — with zero-downtime upgrades\n\n - Own CI/CD, Infrastructure as Code, automations, and containerized deployments across the platform\n\n - Drive down infrastructure cost per request while traffic and data volume grow\n\n - Build the observability that keeps latency, throughput, and reliability predictable and define the SLIs, SLOs, and customer-facing SLAs we hold ourselves to.\n\n - Build and support our enterprise controls — SSO/SAML, RBAC, audit logging, tenant isolation, private networking, and the infrastructure behind SOC 2 and customer security reviews\n\n - Work directly with product and search engineers to productionize new services, retrieval, and ML workloads\n\n - Own the incident lifecycle with engineers — from on-call and triage process to postmortems and resolution.\n\n\n\n\nWHAT WE'RE LOOKING FOR\n\n - You've operated stateful distributed systems on Kubernetes at real scale — not just stateless services\n\n - You have deep experience with a major cloud (e.g. GCP, AWS, Azure), Docker, and Terraform; MLOps or ML-serving infrastructure experience (GPU workloads, model deployment pipelines) is a plus\n\n - You've run large-scale, data-heavy systems in production — search platforms, crawling or ingestion pipelines, or comparable. Hands-on experience operating Vespa https://github.com/vespa-engine/vespa is a strong plus.\n\n - You care about latency, cost, and reliability in equal measure\n\n - Experience with security and compliance infrastructure (SSO/SAML, audit logging, network isolation, SOC 2) is a strong plus — especially for the Core Platform focus\n\n - You're comfortable owning ambiguous problems and turning them into shipped infrastructure\n\n\n\n\nWHAT WE'RE NOT LOOKING FOR\n\n - Someone who needs a fully-specced ticket to start\n\n - Someone who wants to specialize narrowly and hand off everything else\n\n - Someone who optimizes for process over shipping\n\n\n\n\nA NOTE ON PACE\n\nWe operate at an absurd level of urgency because the window for what we're building won't stay open forever. If that excites you, keep reading. If it doesn't, no hard feelings — but this role probably isn't for you.\n\n\n\n\nBENEFITS \u0026 PERKS\n\n\n\n\nAVAILABLE TO ALL EMPLOYEES\n\n - Salary that makes sense — $246,000–$271,000/year, based on impact, not tenure\n\n - Own a piece — Gain competitive equity in what you're helping build\n\n - Generous PTO — 15 days mandatory, anything after 24 days, just ask (holidays excluded); take the time you need to recharge\n\n - Parental leave — 12 weeks fully paid, for all parents\n\n - Wellness stipend — $100/month for the gym, therapy, massages, or whatever keeps you human\n\n - Learning \u0026 Development — Expense up to $1,000/year toward anything that helps you grow professionally\n\n - Team offsites — A change of scenery, minus the trust falls\n\n - Sabbatical — 3 paid months off after 4 years, do something fun and new\n\n\n\n\nAVAILABLE TO US-BASED FULL-TIME EMPLOYEES\n\n - Full coverage, no red tape — Medical, dental, and vision (100% for employees, 50% fo","salary_min":246000,"salary_max":271000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["llm","distributed-systems","agents","mlops","search","cloud","devops"],"apply_url":"https://jobs.ashbyhq.com/firecrawl/fe538f2b-7dd5-4d8d-941e-f8014a911652/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-20T22:04:31.381Z","expires_at":"2026-09-29T13:45:44.202795Z","created_at":"2026-08-25T18:32:19.567715Z","updated_at":"2026-08-30T13:45:44.333747Z","company_name":"Firecrawl","company_slug":"firecrawl","company_logo_url":"https://www.google.com/s2/favicons?domain=firecrawl.dev\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/3707734c-f7ec-4eee-bd06-3bd04c3d3355"},{"id":"95641443-c7f1-443a-b4d7-ef763360667d","company_id":"52f44519-9f93-4eac-ae0b-8be13e385ebe","title":"Machine Learning Engineer","slug":"machine-learning-engineer-a0756d7f","description":"MACHINE LEARNING ENGINEER\n\n \n\nYou'll build the ML behind Firecrawl — the models and the systems that serve them. That starts with search: training and shipping the ranking and relevance models for one of our fastest-growing products, then extending that work across extraction quality and LLM-driven features. You'll also own how we measure: A/B testing launches and building the experimentation frameworks the whole team ships against. If you ship models into production — whether your title says ML engineer or data scientist — this is for you.\n\n \n\nSalary Range: $250,000–$290,000/year\n\nEquity Range: Competitive equity — details shared during the process.\n\nLocation: San Francisco, CA (Onsite)\n\nJob Type: Full-Time \n\nExperience: 3+ years building ML or data-heavy systems in production \n\nVisa: Must be legally authorized to work in the United States. We're not able to sponsor visas right now, though that may change down the line.\n\n\n\n\nABOUT FIRECRAWL\n\nFirecrawl is the easiest way to turn the web into data AI agents can use. One API call converts any URL into clean, LLM-ready markdown or structured data - the boring-hard problem everyone building with LLMs eventually hits, solved.\n\nWe hit 8 figures in ARR in year one and more than doubled it in year two. We have 170k+ GitHub stars, and developers, agents, and category-defining AI companies build on us every day. Growth like this is rare, and we're just getting started.\n\nWe're a small team punching far above our weight. Everyone here owns a real piece of the product and company, end to end, and runs it themselves - no hiding behind process or headcount.\n\nThis is a place for people who want to work at the frontier: an AI company building the infrastructure other AI companies run on, not one bolting AI onto an existing product. We move fast, go deep, and are building the tools superintelligence will rely on to gather data from the web.\n\n\n\n\nWHAT YOU'LL DO\n\n - Improve ranking and relevance for Firecrawl Search — from feature engineering to model training to production\n\n - Build and tune models for learning-to-rank, query understanding, and LLM-driven retrieval\n\n - Extend ML across Firecrawl's products — extraction quality, content classification, and evaluation of LLM-driven features\n\n - Mine query logs and behavioral data at scale to find where our products win and where they fail\n\n - Build the data pipelines that turn web-scale crawl and query data into training data and features\n\n - Work hands-on with platform, search engineers and cloud DevOps to get models running fast and cheap in production\n\n - Design and formulate our testing strategy — the A/B testing frameworks and offline evaluation the team ships against\n\n - Partner on product launches across Firecrawl: define success metrics, run the experiments, and make the ship/no-ship call on evidence\n\n - Report on how releases perform post-launch and turn the findings into the next iteration\n\n\n\n\nWHAT WE'RE LOOKING FOR\n\n - You've shipped ML models into production systems and owned them after launch — deploying, monitoring, and retraining them, not handing them off\n\n - You have real ranking or relevance-modeling experience — learning-to-rank, recommendations, or search quality\n\n - You're comfortable in large, data-heavy systems: query logs, pipelines, and datasets that don't fit in memory\n\n - You write production-quality code (Python at minimum) and can work inside a real backend codebase\n\n - You're rigorous about measurement — you've designed and analyzed A/B tests and know when a lift is real\n\n - You can communicate results clearly to the team — what shipped, what moved, and what to do next\n\n\n\n\nNICE TO HAVE\n\n - MLOps experience — MLflow, experiment tracking, model registries, or feature stores; Kubernetes is a plus\n\n - Experience building or standardizing an experimentation framework at a previous company\n\n - Experience with embedding models, vector retrieval, or LLM-based relevance evaluation\n\n - Experience evaluating LLM outputs at scale — quality scoring, structured-extraction accuracy, or agent behavior\n\n - Spark or similar large-scale data processing experience\n\n\n\n\nWHAT WE'RE NOT LOOKING FOR\n\n - A pure statistician or analyst who needs an engineering team to productionize their work\n\n - Someone who wants to specialize narrowly and hand off everything else\n\n - Someone who optimizes for process over shipping\n\n\n\n\nA NOTE ON PACE\n\nWe operate at an absurd level of urgency because the window for what we're building won't stay open forever. If that excites you, keep reading. If it doesn't, no hard feelings — but this role probably isn't for you.\n\n\n\n\nBENEFITS \u0026 PERKS\n\n\n\n\nAVAILABLE TO ALL EMPLOYEES\n\n - Salary that makes sense — $250,000–$290,000/year, based on impact, not tenure\n\n - Own a piece — Gain competitive equity in what you're helping build\n\n - Generous PTO — 15 days mandatory, anything after 24 days, just ask (holidays excluded); take the time you need to recharge\n\n - Parental l","salary_min":250000,"salary_max":290000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["search","data-pipeline","embeddings","mlops","agents","llm","machine-learning"],"apply_url":"https://jobs.ashbyhq.com/firecrawl/72f9dc1d-65db-48c9-b3d9-c6ccdb997006/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-20T21:40:10.101Z","expires_at":"2026-09-29T13:45:43.966664Z","created_at":"2026-08-25T18:32:19.563403Z","updated_at":"2026-08-30T13:45:44.23876Z","company_name":"Firecrawl","company_slug":"firecrawl","company_logo_url":"https://www.google.com/s2/favicons?domain=firecrawl.dev\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/95641443-c7f1-443a-b4d7-ef763360667d"},{"id":"e83f9b8b-565f-4a94-b2f4-03b79a6f7843","company_id":"2114efab-ea67-411b-bfb8-7899153105f3","title":"Member of Technical Staff, Site Reliability Engineer","slug":"member-of-technical-staff-site-reliability-engineer-369fc8ef","description":"Overview\n\nInferact's mission is to grow vLLM as the world's AI inference engine and accelerate AI progress by making inference cheaper and faster. Founded by the creators and core maintainers of vLLM, we sit at the intersection of models and hardware, a position that took years to build.\n\nAbout the Role\n\nWe're looking for a Site Reliability Engineer to help make vLLM-powered inference systems reliable, observable, and operationally simple at production scale. This role is for someone who thinks about failure before launch, designs systems that are easier to operate, and knows how to turn incidents into durable improvements rather than one-off fixes.\n\nYou'll work across engineering and infrastructure to define SLOs, improve monitoring and alerting, strengthen incident response, drive post-mortems, and reduce operational risk before it reaches users. Your work will directly impact the reliability, availability, and production readiness of the systems powering AI inference at scale.\n\n\n\nSkills and Qualifications\n\nMinimum qualifications:\n\n - Bachelor's degree or equivalent experience in computer science, engineering, systems, infrastructure, or similar.\n\n - Strong experience operating production systems with meaningful traffic, user impact, or infrastructure criticality.\n\n - Deep understanding of SLOs, SLIs, error budgets, alerting, incident response, and post-mortem processes.\n\n - Experience live-fighting major production incidents, including mitigation, root cause analysis, escalation, and follow-through on prevention work.\n\n - Strong Linux, networking, systems debugging, observability, and distributed systems fundamentals.\n\n - Ability to design operationally simple systems and identify likely failure modes before launch.\n\n - Strong programming or scripting ability in Python, Go, Bash, or similar for automation, tooling, and reliability improvements.\n\nPreferred qualifications:\n\n - Experience supporting ML infrastructure, inference systems, GPU workloads, Kubernetes-based platforms, or high-scale backend services.\n\n - Experience building or improving observability systems using metrics, logs, traces, dashboards, alerts, and runbooks.\n\n - Experience with Kubernetes, Docker, Terraform, cloud infrastructure, service meshes, CI/CD systems, or production deployment platforms.\n\n - Experience driving incident review culture, post-mortem processes, reliability reviews, and prevention-oriented engineering work.\n\n - Ability to partner with engineering teams to improve service design, release safety, capacity planning, and operational readiness.\n\nBonus points if you have:\n\n - Owned reliability for high-throughput, latency-sensitive, or mission-critical production systems.\n\n - Supported AI inference, model serving, GPU clusters, ML platforms, or distributed serving infrastructure.\n\n - Built automation that reduced toil, improved recovery time, or prevented repeat incidents.\n\n - Led incident response for severe outages with clear communication across engineering and leadership.\n\n - Created practical SLOs, dashboards, alerts, runbooks, or release gates that improved production reliability.\n\n\n\nLogistics\n\n - Location: This role is based in San Francisco, California. Will consider remote in the US for exceptional candidates.\n\n - Compensation: Depending on background, skills, and experience, the expected annual salary range for this position is $200,000 - $400,000 USD + equity.\n\n - Visa sponsorship: We sponsor visas on a case-by-case basis.\n\n - Benefits: We offers generous health, dental, and vision benefits as well as 401(k) company match.","salary_min":200000,"salary_max":400000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","mlops","gpu","cloud","distributed-systems","devops","research"],"apply_url":"https://jobs.ashbyhq.com/inferact/ad992ead-2a9a-4694-8fca-0504354548cd/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-20T18:25:22.552Z","expires_at":"2026-09-29T13:41:27.521095Z","created_at":"2026-08-25T18:30:22.617589Z","updated_at":"2026-08-30T13:41:27.654002Z","company_name":"Inferact","company_slug":"inferact","company_logo_url":"https://www.google.com/s2/favicons?domain=inferact.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e83f9b8b-565f-4a94-b2f4-03b79a6f7843"},{"id":"90971ca5-fcc8-4b69-835c-9a462afd1f5e","company_id":"83c597c2-a4b2-4517-99df-1ac8c90756d5","title":"Software Engineer II, MLOps Framework","slug":"software-engineer-ii-mlops-framework-6090a7e5","description":"About the Company   \n At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business.   A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners.  Now a part of the Daimler family , we are focused solely on developing software for automated trucks to transform how the world moves freight.   Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer.   \n Meet the Team   \n As a Software Engineer on the ML Ops Framework \u0026 Conversion team, you will own the pipelines that take models from research to production on edge hardware, including model conversion, compilation, benchmarking, and release. Our team is comprised of engineers with deep expertise in ML and RL frameworks, embedded systems, and autonomous driving — united by a focus on getting models from development into the real world reliably and at scale. The ML Ops Framework \u0026 Conversion team is responsible for the full model conversion process — from architecting TensorRT pipelines to maintaining the model release registry across platforms. In this role, you will work closely with perception and safety teams to ensure every model that ships meets strict latency and accuracy requirements for autonomous trucking. \n What You’ll Do   \n \n Architect and implement model conversion and compilation pipelines using tools such as ONNX, TensorRT, and torch.compile for deployment on edge devices (e.g., NVIDIA Orin).\n Maintain and evolve the model release registry, ensuring traceability and reproducibility across model versions and target platforms.\n Perform rigorous latency benchmarking and model quality parity evaluations to validate that deployed models meet safety-critical performance requirements.\n Compare metrics across platforms to verify accuracy and latency compliance before release.\n Communicate and collaborate with model development teams and broader stakeholders, ensuring your findings translate into reliable, actionable outcomes across the organization.\n \n What You’ll Need to Succeed   \n \n Bachelor’s Degree in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrates competences and technical proficiencies typically acquired through 4+ years of experience or;\n Master’s Degree in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrates competences and technical proficiencies typically acquired through 0-3+ years of experience.\n Extensive experience with model conversion and compilation pipelines (ONNX, TensorRT, torch.compile) and performing rigorous latency benchmarking and quality parity validation.\n Hands-on experience deploying and testing models on edge hardware (e.g., NVIDIA Orin or similar embedded platforms).\n Experience maintaining a model release registry in a production environment.\n Ability to compare and interpret performance metrics across hardware platforms to validate models against strict latency and accuracy requirements.\n \n Bonus Points \n \n Expertise in model quantization (PTQ, QAT) and mixed-precision inference (INT8, FP8, FP4, BF16/FP16).\n Experience releasing multi-target models across heterogeneous platforms.\n Familiarity with SOTA autonomous driving perception algorithms — temporal 3D object detection, BEV, 3D Occupancy Networks — and multi-modal sensor fusion (vision, LiDAR, radar).\n C++ and/or CUDA kernel development.\n \n Perks of Being a Full-time Torc’r   Torc cares about our team members and we strive to provide benefits and resources to support their health, work/life balance, and future. Our culture is collaborative, energetic, and team focused. Torc offers:       \n \n A competitive compensation package that includes a bonus component and stock options   \n \n \n 100% paid medical, dental, and vision premiums for full-time employees   \n \n \n 401K plan with a 6% employer match   \n \n \n Flexibility in schedule and generous paid vacation (available immediately after start date)   \n \n \n AD+D and Life Insurance   \n \n At Torc, we’re committed to building a diverse and inclusive workplace. We celebrate the uniqueness of our Torc’rs and do not discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, veteran status, or disabilities.   Even if you don’t meet 100% of the qualifications listed for this opportunity, we encourage you to apply.   \n Our compensation reflects the cost of labor across several geographic markets. Pay is based on a number of factors and may vary depending on job-related knowledge, skills, and experience. Torc's total compensation package will also include our corporate bonus and stock option plan. Dep","salary_min":139000,"salary_max":166800,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"mid","tags":["mlops","computer-vision","gpu","robotics","payments","autonomous-vehicles"],"apply_url":"https://job-boards.greenhouse.io/torcrobotics/jobs/8728727002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-20T16:09:33Z","expires_at":"2026-09-29T13:36:08.277033Z","created_at":"2026-08-25T18:27:50.579783Z","updated_at":"2026-08-30T13:36:08.417728Z","company_name":"Torc Robotics","company_slug":"torc-robotics","company_logo_url":"https://www.google.com/s2/favicons?domain=torc.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/90971ca5-fcc8-4b69-835c-9a462afd1f5e"},{"id":"f870f515-f436-4a0b-b447-09b7e3201f15","company_id":"a0000000-0000-0000-0000-000000000001","title":"Staff+ Software Engineer, Claude Managed Agents","slug":"staff-software-engineer-claude-managed-agents-8e1781b3","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 We are looking for experienced backend and distributed systems engineers to join the Agentic Systems team within our Platform organization. Agentic Systems builds Claude Managed Agents: the hosted platform for building, running, and scaling production agents on Claude. Instead of every developer hand-rolling an agent loop, sandboxed execution, state management, credential handling, and error recovery — and reworking all of it with every model release — Managed Agents pairs an Anthropic-built agent harness with production infrastructure for sessions, environments, tools, memory, and permissions, exposed through a small set of composable APIs designed to stay stable as models and harnesses evolve. It powers agentic products inside Anthropic as well as those built by customers on the Claude Platform.\n Managed Agents is in public beta and growing quickly, and this is still an early team with a lot of surface area left to define. You'll drive 0 → 1 efforts from ideation through GA, own systems end to end from API design through operations, and partner closely with product, research, developer experience, and go-to-market teams to figure out what \"managed\" should mean for the next generation of agents. You should be comfortable going deep on hard distributed systems problems, care about APIs as a product in their own right, and be motivated by turning ambiguous ideas into high-quality, shipped platform capabilities that other engineers build their products on.\n What you'll do\n Scale the platform. Managed Agents runs long-lived, stateful sessions that execute autonomously for minutes or hours/days, persist through disconnections, and resume cleanly — across Anthropic-hosted sandboxes, self-hosted environments on customer infrastructure, and other clouds. You'll design and operate the systems underneath that: durable session and event storage, sandbox orchestration, streaming, scheduling, and multi-tenant isolation. Reliability, latency, and cost efficiency are product features here, and you'll own them in production.\n Evolve the harness — and prove it with evals. The harness is the loop that calls Claude, routes tool calls, manages context (caching, compaction, memory), and recovers from errors. Harnesses encode assumptions about what the model can't yet do on its own, and those assumptions go stale as models improve. You'll work alongside research to revisit them with each model generation, build the eval infrastructure that measures harness quality against research baselines and real customer workloads, and hold the bar that lets us say our harness gets the most out of Claude.\n Help builders get the most out of Claude. Our customers — internal and external — are building agents both as products for their users and to transform their own operations. You'll ship the capabilities that raise the ceiling on what those agents can do: outcome-driven execution where developers specify success criteria and a budget and Claude iterates until it gets there, multi-agent orchestration, memory, and the observability and tracing that make long-running agents debuggable. The goal is the highest intelligence per dollar of any agent platform, delivered safely.\n Design APIs that outlast their implementations. Agents, environments, sessions, vaults, and event streams are interfaces thousands of developers build against and that our own products depend on. You'll shape those primitives — versioning, ergonomics across API, SDK, and CLI, sensible defaults, escape hatches — with the expectation that the implementations underneath will change many times while the contracts hold.\n You might be a good fit if you:\n \n Have a minimum of 8 years of practical experience as a backend, distributed systems, or infrastructure engineer\n Have built and operated stateful, long-running, or high-throughput systems in production — workflow orchestration, streaming, storage, container or job orchestration — and can reason rigorously about durability, consistency, failure modes, and cost\n Have strong product sense and treat API design as a craft; you care about the developer on the other side of the interface and can ideate and execute product strategy with cross-functional partners in new domains\n Are excited by 0 → 1 work and comfortable navigating ambiguity, and have ideally operated in both early-stage and more mature team or company settings\n Use Claude or other AI tools as a core part of how you build software, and have opinions about what makes an agent harness good\n Take full ownership of your work — from design through build, deployment, and operations (including on-ca","salary_min":405000,"salary_max":485000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["mlops","api-design","llm","distributed-systems","agents","alignment"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5395767008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-20T15:55:19Z","expires_at":"2026-09-29T13:30:38.262536Z","created_at":"2026-08-25T18:26:20.027221Z","updated_at":"2026-08-30T13:30:38.402692Z","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/f870f515-f436-4a0b-b447-09b7e3201f15"},{"id":"fcda730f-0dab-4f16-a21e-6906a54e407c","company_id":"3029e985-56bf-4ac2-9ae1-df4cdd53b12f","title":"AI DevOps Engineer","slug":"ai-devops-engineer-40d84475","description":"About Zscaler \n Zscaler accelerates digital transformation to ensure our customers can be more agile, efficient, resilient, and secure. As an AI-forward enterprise , we are constantly pushing the envelope, leveraging the world’s largest security data lake to power our cloud-native Zero Trust Exchange platform. This innovation protects our customers from cyberattacks and data loss by securely connecting users, devices, and applications in any location.\n Here, impact in your role matters more than title and trust is built on results. We say, impact over activity. We seek innovators who actively use AI to amplify their impact and who thrive in an environment where we leverage intelligent systems to stay ahead of evolving threats. We believe in transparency and value constructive, honest debate —we’re focused on getting to the best ideas, faster. We build high-performing teams that can make an impact quickly and with high quality. To do this, we are building a culture of execution centered on customer obsession , collaboration, ownership, and accountability.\n We value high-impact, high-accountability with a sense of urgency where you’re enabled to do your best work and embrace your potential. If you’re driven by purpose, thrive on solving complex challenges, and want to be part of the team that’s helping to secure the AI age, we invite you to bring your talents to Zscaler and help shape the future of cybersecurity.\n Role  \n We are looking for an AI DevOps Engineer to join our team. This is a Remote within the United States (with a hybrid preference for San Jose, CA) role, reporting to the Manager, IT Cloud Operations in the Cloud Platform Engineering department. Our team builds and operates the internal cloud platform that powers Zscaler's product and corporate infrastructure across AWS, GCP, and Azure. In this role, you will design and ship automation that provisions cloud environments, enforces security baselines, and integrates AI-assisted tooling and agentic workflows to accelerate delivery.\n What you’ll do (Role Expectations) \n \n Build and extend Day 1 automation, including infrastructure provisioning pipelines, account vending, golden repo templates, and CI/CD components\n Build and extend Day 2 automation for lifecycle management, upgrade pipelines, dependency scanning, drift detection, and automated remediation workflows\n Write and maintain Terraform modules, GitLab CI/CD components, and Python automation to establish the platform's paved road\n Integrate AI and agentic tooling into operational workflows to reduce manual toil and increase operational consistency\n Collaborate with Security, IAM, Network, and FinOps teams to translate cross-functional requirements into automated guardrails\n \n Who You Are (Success Profile) \n \n You thrive in ambiguity. You are comfortable building the path as you walk it, viewing dynamic environments as raw material to build something meaningful.\n You act like an owner. Your passion for the mission fuels your bias for action, seamlessly navigating between high-level strategy and hands-on execution.\n You are a problem-solver. You seek out challenges because you are energized by finding solutions, knowing that solving hard problems delivers maximum impact.\n You are a high-trust collaborator. You embrace a challenge culture by giving and receiving ongoing feedback with clarity, respect, and candor.\n You are a learner. You bring a true growth mindset and actively seek feedback to continuously develop yourself and support your team.\n \n What We’re Looking for (Minimum Qualifications) \n \n Demonstrated curiosity and active exploration of AI tools, with a proven history of integrating new technologies to enhance daily workflows and augment problem-solving\n 5+ years of experience building and operating multi-cloud infrastructure at scale across major cloud platforms\n Hands-on expertise with Terraform, including module design, state management, and CI-driven workflows\n Proficiency in scripting and automation using Python, Bash, or equivalent languages\n Working knowledge of CI/CD pipeline design and Kubernetes operations\n Understanding of identity federation, secrets management, and least-privilege security patterns\n \n What Will Make You Stand Out (Preferred Qualifications) \n \n Experience with multi-account governance tooling such as AWS Control Tower, Organizations, SCPs, or RCPs\n Experience with GitOps patterns and automated infrastructure lifecycle tooling such as Renovate, Dependabot, or ArgoCD\n Experience with MLOps frameworks: kubeflow, ML flow, and model services like Bedrock, Sagemaker, Vertex AI, Gemini Enterprise\n \n #LI-Remote #LI-YC2\n Zscaler’s salary ranges are benchmarked and are determined by role and level. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations and could be higher or lower based on a multitude of factors, including job-related skills, experience, and ","salary_min":140000,"salary_max":175000,"location":"Remote (US)","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["fine-tuning","security","data-pipeline","mlops","agents","cloud","devops"],"apply_url":"https://job-boards.greenhouse.io/zscaler/jobs/5208829007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-20T15:00:13Z","expires_at":"2026-09-29T13:39:53.846816Z","created_at":"2026-08-25T18:29:22.952569Z","updated_at":"2026-08-30T13:39:53.98003Z","company_name":"Zscaler","company_slug":"zscaler","company_logo_url":"https://www.google.com/s2/favicons?domain=zscaler.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/fcda730f-0dab-4f16-a21e-6906a54e407c"},{"id":"e162808c-df72-4c08-9993-0438f336d9ac","company_id":"e3915539-5a8f-4461-9f26-06366a918674","title":"Staff AI Infrastructure Engineer","slug":"staff-ai-infrastructure-engineer-f1e0827b","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 ABOUT THE TEAM   \n The Air Dominance \u0026 Strike team at Anduril develops aerial and multi-domain robotic systems. The team is responsible for taking products like Fury (unmanned fighter jet) and Barracuda (air-breathing cruise missile) from concept to product. The team also develops Lattice for Mission Autonomy, Anduril’s premier software platform that enables masses of Fury, Barracuda, and other first and third party robots to collaborate across various missions. We work in close coordination with specialist teams like Perception, Motion Planning, Hardware, and Test Engineering to solve some of the hardest problems facing our customers. We are looking for software engineers and roboticists excited about creating a powerful autonomy software stack that includes computer vision, motion planning, SLAM, controls, estimation, and secure communications.   \n ABOUT THE JOB   \n We are looking for a founding  Staff AI Infrastructure Engineer  to architect, build, and scale the end-to-end machine learning platform that powers Anduril’s autonomous systems.    \n As a Staff Engineer, you will own the technical roadmap for our ML platform. You will build the robust infrastructure, MLOps tooling, and systems architecture required to train, evaluate, host, and serve complex AI models (including LLMs, computer vision, and RL agents) in both cloud environments and air-gapped, offline tactical edge networks. You will be a force multiplier for our AI Research Scientists, optimizing their experimentation velocity and managing the lifecycle of terabytes of multi-modal sensor and simulation data. Over time, you will help recruit, mentor, and expand this infrastructure engineering team.   \n WHAT YOU’LL DO   \n \n Design, build, and maintain our foundational training, orchestration, and experimentation infrastructure to support state-of-the-art model development. \n Actively identify, measure, and eliminate bottlenecks in the ML research lifecycle. Build highly automated tools for hyperparameter tuning, model profiling, and experimentation tracking.\n Design and scale robust, high-performance ETL pipelines capable of processing terabytes of multi-modal data (video, camera feeds, radar, flight telemetry, and simulation logs) captured from physical assets and test sites.\n Architect high-throughput, low-latency model serving frameworks optimized for both scalable cloud environments and air-gapped, resource-constrained tactical edge environments. Build CI/CD pipelines for ML models with automated validation, canary deployments, and rollback capabilities.\n Build robust, automated pipelines for continuous evaluation, model validation, and reinforcement learning alignment loops (RLHF/DPO) to guarantee model safety and predictability in high-stakes environments.\n Work closely with AI Researchers, Computer Vision teams, and platform engineers to design unified infrastructure standards across the company's autonomous systems programs.   \n \n REQUIRED QUALIFICATIONS   \n \n 7+ years of software engineering experience with a proven track record of designing, building, and operating production-scale machine learning systems and platforms (MLOps). \n Proficient in Python, Go, C++, or similar backend languages. Deep understanding of ML systems design, memory management, and distributed computing.\n Deep experience with containerized deployments (Docker, Kubernetes), GPU scheduling/orchestration, and distributed training frameworks (e.g., PyTorch Distributed, Ray, Slurm, or Megatron-LM).\n Hands-on experience building distributed data pipelines (ETL) and managing massive datasets (terabytes of unstructured/multi-modal sensor data).\n Experience setting technical direction, leading complex system migrations, and mentoring senior engineers.\n Eligible to obtain and maintain an active U.S. Top Secret security clearance.   \n \n PREFERRED QUALIFICATIONS   \n \n Experience building and running ML infrastructure, model serving, or software registries within secure, air-gapped, or highly regulated environments (e.g., IL5/IL6, GovCloud). \n Experience specifically building training and evaluation platforms for Large Language Models, Generative AI architectures, or Reinforcement Learning (RL) pipelines.\n Experience profiling ","salary_min":220000,"salary_max":292000,"location":"Costa Mesa, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["computer-vision","mlops","llm","cloud","distributed-systems","robotics","data-pipeline","gpu"],"apply_url":"https://boards.greenhouse.io/andurilindustries/jobs/5212860007?gh_jid=5212860007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-19T16:14:11Z","expires_at":"2026-09-29T13:37:28.517737Z","created_at":"2026-08-25T18:28:19.592013Z","updated_at":"2026-08-30T13:37:28.652109Z","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/e162808c-df72-4c08-9993-0438f336d9ac"},{"id":"002ccf4a-8f74-48a3-9375-c175114691c0","company_id":"2721f049-2cf2-4e3e-82d0-8d8df89c8f90","title":"Manager, ML Solutions Architecture - Token Factory","slug":"manager-ml-solutions-architecture-token-factory-af263b29","description":"About Nebius: \n Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure.\n Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.\n Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R\u0026D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R\u0026D.\n The role \n This position sits within Nebius Token Factory, our serverless platform for running and customizing open-source LLMs in production. Token Factory allows for serverless inference and fine-tuning backed by in-house optimizations like custom speculative decoding, quantization, cache-aware routing and dedicated endpoints. Customers come to us to move from prototype to scaled production without the cost and complexity of building and tuning their own inference stack.\n Our Solutions Architects own the technical delivery of customer engagements: deploying open-source models, tuning the serving stack, benchmarking against the customer's success criteria, and carrying the technical relationship through to production. Sales Engineers qualify and scope the opportunity; SAs execute it. Technical Account Managers take it from production onward.\n We're looking for a Manager, ML Solutions Architecture to lead our US regional SA teams. Your scope is PoC delivery and post-sales technical support : the people who do it, the standard they do it to, and the operating cadence that keeps it predictable. You will report to the Head of Solutions Architecture and partner with a peer manager in the other region.\n We expect you to push people when they need pushing, make the uncomfortable call when deliverables don't land even though the effort was real, and take ownership of the documentation work: implement and monitor the following of the guidelines, ticket hygiene, and making sure the team actually uses all three.\n You're welcome to work remotely from the United States. \n Your responsibilities will include:   \n Lead the team \n \n Manage a team of 4 Solutions Architects, with continued growth planned: 1:1s, goal setting, performance reviews, promotion cases, and individual growth plans\n Build an accurate picture of each SA 's strengths, gaps, and preferences, and allocate accounts and engagements against both expertise and interest\n Run the cadence that surfaces blockers early, then take them to the development, product, and business teams that can clear them, and stay on them until they do\n Hold people to outcomes: distinguish effort from delivered results, say so plainly when the two diverge, and reflect it in ratings and compensation decisions\n Onboard new joiners through to their first independently delivered engagement\n Coach SAs into stronger engineers and stronger communicators, and make deliberate calls about who is ready for more scope\n \n Own delivery \n \n Be accountable for your team's delivery outcomes: time from PoC kick-off to first optimized dedicated endpoint, success-criteria hit rate, and the quality of the technical relationship after the customer goes to production\n Review technical work before it reaches the customer: benchmarking methodology, serving configurations, results, closure documents; catch the wrong conclusion drawn from a metrics artifact before a customer sees it\n Ensure staffing and escalation coverage across accounts and timezones, including post-sales request load that does not respect sprint boundaries\n Call infeasibility early and with evidence, rather than letting the team burn iterations against requirements the platform cannot meet today\n \n Own the operating system of the team \n \n Maintain and extend the team's documentation: responsibilities, runbooks, guides, onboarding, definitions of done, engagement closure templates. Keep it accurate and reachable; link, don't copy\n Get it used , not just written: documentation nobody reads is a cost, not an asset\n Keep the ticket tracker the system of record, so PoC and production status is readable without asking anyone\n Instrument the work: define and report the metrics that show whether delivery is getting faster and more reliable over time\n \n Work across teams \n \n Development and research teams: convert recurring customer pain into prioritized platform work, and represent the customer's technical reality in roadmap discussions\n Pre-sales: hold the scoping-to-execution boundary: push back on under-scoped engagements, and feed feasibility signal back upstream\n Account management: make production handoffs uneventful, and keep post-sales technical requests moving\n Business and leadership: give a","salary_min":228000,"salary_max":285000,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["generative-ai","fine-tuning","mlops","cloud","llm"],"apply_url":"https://careers.nebius.com/?gh_jid=4952080101","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-19T14:34:47Z","expires_at":"2026-09-29T13:45:04.491148Z","created_at":"2026-08-25T18:31:43.084355Z","updated_at":"2026-08-30T13:45:04.621715Z","company_name":"Nebius","company_slug":"nebius","company_logo_url":"https://www.google.com/s2/favicons?domain=nebius.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/002ccf4a-8f74-48a3-9375-c175114691c0"},{"id":"e528a562-2c86-4037-b54d-8daa64393e83","company_id":"47c8818e-9a45-4180-8d96-931d2774d36b","title":"Staff Machine Learning Model Risk Specialist","slug":"staff-machine-learning-model-risk-specialist-dbe9dbcd","description":"About Upstart \n At Upstart, we’re united by a mission that matters: to radically reduce the cost and complexity of borrowing for all Americans. Every day, we bring creativity, experimentation, and advanced AI to reshape access to credit, helping millions move forward financially with clarity and confidence.\n As the leading AI lending marketplace, we partner with banks and credit unions to expand access to affordable credit through technology that’s both radically intelligent and deeply human. Our platform runs over one million predictions per borrower using more than 3,000 signals, powering smarter, fairer decisions for millions of customers. But the numbers only hint at the impact. Every idea, every voice, and every contribution moves us closer to a world where credit never stands between people and their financial progress.\n We’re proudly digital-first, giving most Upstarters the flexibility to do their best work from wherever they thrive, alongside teammates across 80+ cities in the US and Canada. Digital-first doesn’t mean distant. We’re intentional about in-person connection through team onsites, planning sessions, and moments that spark creativity and trust. And whether you choose to work primarily from home or collaborate in-person from one of our offices in Columbus, Austin, the Bay Area, or New York City (opening Summer 2026), you’ll have the support to work in the way that works best for you.\n If you’re energized by tackling meaningful problems, excited to innovate with purpose, and motivated by work that truly matters, we’d love to hear from you.\n The Team:  \n Upstart’s Model Risk team is responsible for ensuring that the risk of models — including all models impacting the new Upstart Bank — is well-understood, monitored, and mitigated. For years, machine learning (ML) models have been the key, differentiating technology at Upstart and an exciting area of focus for the team, but we are also expanding our scope to include all modeling methodologies and Generative AI applications across the Bank. This work is essential for Upstart’s internal risk management, ensuring that our models help us make better decisions and maintain credibility with external stakeholders such as our regulators and capital providers. The team’s focus is on articulating sound model risk management principles and implementing them in collaboration with our peers on Upstart’s Risk and Machine Learning teams. This work also includes explaining our models to stakeholders, supporting external validations, and conducting analyses to reinforce our goals.\n As a Staff Model Risk Specialist at Upstart, you will independently execute core components of the model risk management program supporting Upstart Bank. You will oversee risk across a diverse and growing inventory of models and Generative AI applications, including sophisticated machine learning models used in lending and other models supporting areas such as fraud, compliance, finance, capital and liquidity, servicing, and operational risk.\n This presents a unique opportunity to help build a comprehensive model risk management program for a new bank. You will evaluate model and GenAI application documentation, monitoring, governance, and risk assessments while partnering with developers, business sponsors, and risk stakeholders to identify and address emerging risks. You will apply a risk-based approach across technologies that range from traditional statistical methods to advanced machine learning and GenAI systems, adapting your review to their different purposes, complexities, and risk profiles. You will also translate complex technical concepts into clear, decision-useful information for audiences with varying levels of technical expertise.\n  \n How you’ll make an impact \n \n Partner with Machine Learning teams, GenAI application developers, business sponsors, and other stakeholders to maintain accurate inventories, risk assessments, documentation, monitoring reports, and supporting governance materials for models and GenAI applications affecting Upstart Bank.\n Review methodologies, assumptions, data inputs, system designs, performance measures, controls, and limitations to provide effective challenge and identify areas requiring further analysis or remediation.\n Apply a risk-based approach to evaluate a broad range of quantitative methods and technologies, from traditional statistical and financial models to complex machine learning models and GenAI applications.\n Conduct and document model risk assessments, monitoring reviews, and targeted quantitative analyses that support internal policies and regulatory expectations.\n Help develop practical governance approaches for new and rapidly evolving technologies, particularly machine learning and GenAI applications for which risks, evaluation methods, and industry practices continue to evolve.\n Respond to model- and GenAI-related questions from regulators, lending partners, and other external stakeh","salary_min":140300,"salary_max":175000,"location":"United States","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"lead","tags":["generative-ai","deep-learning","rag","mlops","agents","machine-learning"],"apply_url":"https://careers.upstart.com/jobs?gh_jid=8140033","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-18T19:21:29Z","expires_at":"2026-09-29T13:45:21.327262Z","created_at":"2026-08-25T18:31:45.395594Z","updated_at":"2026-08-30T13:45:21.461964Z","company_name":"Upstart","company_slug":"upstart","company_logo_url":"https://www.google.com/s2/favicons?domain=upstart.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e528a562-2c86-4037-b54d-8daa64393e83"},{"id":"05a2a4c1-155f-4f2a-b2e9-4acdac1c396f","company_id":"74257563-5513-4a8d-a0f7-01f00c59aed6","title":"Staff Machine Learning Engineer, Traffic Intelligence","slug":"staff-machine-learning-engineer-traffic-intelligence-a539b99f","description":"Airbnb was born in 2007 when two hosts welcomed three guests to their San Francisco home, and has since grown to over 5 million hosts who have welcomed over 2 billion guest arrivals in almost every country across the globe. Every day, hosts offer unique stays and experiences that make it possible for guests to connect with communities in a more authentic way. \n The Community You Will Join: \n Our web and API surfaces handle requests from guests and hosts alongside a growing volume of automated agents: AI assistants, crawlers, and scrapers. We build the systems that bring clarity to this traffic, combining in-house ML and vendor signals to decide in real time how to serve billions of daily requests. Anti-bot and anti-scraping detection is our most adversarial mandate, but the wider challenge is full traffic classification: building evaluation frameworks that tell legitimate automation apart from abusive actors, so high-stakes decisions hold up across the fleet.\n The Difference You Will Make: \n You will architect and maintain Airbnb’s end-to-end traffic classification ML systems, balancing high-performance model deployment with rigorous offline data pipelines. Success is measured by your ability to harden edge-traffic policies—targeting reduced bot-incident MTTM—and by establishing rigorous evaluation practices that ensure foundational signal accuracy and evasion-resistance across the fleet. \n A Typical Day:  \n \n Own the complete lifecycle of traffic-scoring models, from problem framing to real-time deployment, managing the adversarial feedback loop to ensure high evasion-resistance and directly drive reductions in bot-incident MTTM.\n Architect robust offline-to-online pipelines that produce certified source-of-truth datasets, establishing rigorous evaluation frameworks—such as stratified benchmarks and leakage-prevention checks—to ensure every model improvement is empirically measurable and defensible.\n Execute model optimization within strict millisecond latency budgets at the internet edge, uniquely balancing inference costs against incremental value while maintaining fleet-wide fail-open behaviors.\n Partner daily with security analysts, data platform engineers, and international infrastructure partners to integrate scoring intelligence into automated mitigation workflows, ensuring global consistency in traffic classification despite regional failovers or CDN updates.\n Serve as the team’s machine learning authority, communicating complex model trade-offs to leadership and cross-functional teams to translate technical research into practical, scalable engineering guidance.\n \n Your Expertise: \n \n 9+ years of applied experience in production ML, specifically within non-stationary, adversarial domains (e.g., traffic integrity, bot mitigation, or fraud) where you have managed the feedback loop against adaptive actors.\n Demonstrated experience architecting scalable, offline-to-online data pipelines that produce certified source-of-truth datasets for low-latency inference systems.\n Strong foundation in rigorous model evaluation, including metrics like ROC/AUC, precision/recall, and calibration, with an ability to communicate complex trade-offs to cross-functional stakeholders.\n Experience with large-scale data engineering (warehouse-scale SQL) and feature engineering on high-volume event streams to build reliable, production-ready modeling pipelines.\n Practical knowledge of internet edge infrastructure (e.g., CDN/load balancer behavior, HTTP/TLS signatures) and their role in verifying foundational signals.\n Proven track record of cross-functional leadership, landing initiatives through shared datasets and consumer contracts while mentoring junior engineers on technical quality and design practices.\n MS/PhD in a quantitative field (e.g., Statistics, ML) or equivalent deep engineering experience, with significant ownership of large-scale systems measuring evasion-resistance.\n Preferred: \n PhD in Statistics, Mathematics, Machine Learning, or a related quantitative discipline.\n Advanced expertise in graph-based coordination or Sybil network detection methods for complex, distributed system analysis.\n Deep experience with causal or econometric methods to model the business impact of false positives on legitimate user traffic.\n Experience implementing Bayesian calibration techniques for handling adversarially-biased, sparse, or imbalanced datasets.\n Familiarity with data governance practices and platform engineering, specifically managing the lifecycle of certified datasets and downstream consumer contracts.\n Exposure to LLM agent tooling and benchmarking, with a focus on optimizing inference costs against latency and value trade-offs.\n \n  \n Your Location: \n This position is US - Remote Eligible. The role may include occasional work at an Airbnb office or attendance at offsites, as agreed to with your manager. While the position is Remote Eligible, you must live in a state where Airbnb, Inc. has a register","salary_min":212000,"salary_max":265000,"location":"United States","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["data-pipeline","llm","mlops","distributed-systems","machine-learning"],"apply_url":"https://careers.airbnb.com/positions/8129371?gh_jid=8129371","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-13T23:22:22Z","expires_at":"2026-09-29T13:39:40.744787Z","created_at":"2026-08-25T18:29:15.56457Z","updated_at":"2026-08-30T13:39:40.88201Z","company_name":"Airbnb","company_slug":"airbnb","company_logo_url":"https://www.google.com/s2/favicons?domain=airbnb.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/05a2a4c1-155f-4f2a-b2e9-4acdac1c396f"},{"id":"e2cc3cda-c6a3-4f53-b1bb-9e02170e612c","company_id":"861968d1-d9f8-4217-9873-ce4b24851abc","title":"Director of Data Science and Bioinformatics","slug":"director-of-data-science-and-bioinformatics-c6f04e5e","description":"This is an exciting opportunity to lead and grow the Data Science and Bioinformatics function supporting Natera's Women's Health and Organ Health product portfolios. In this role, you will build the bioinformatics capability within the Data Science development team, establish scalable AWS cloud infrastructure, and ensure the quality and reproducibility of the genomic algorithms powering our clinical products.\n PRIMARY RESPONSIBILITIES: \n Strategy and Vision \n \n Build and define the Bioinformatics function within the Data Science development team, identify technical tooling gaps, and execute a roadmap to advance genomics-based algorithm development.\n Establish and enforce pipeline and algorithm quality standards, including review processes, validation frameworks, and documentation practices\n \n Infrastructure and Automation \n \n Own and architect scalable AWS-based data science and bioinformatics infrastructure, ensuring quality, reproducibility, and reliable deployment of Next-Generation Sequencing (NGS) algorithms.\n Implement MLOps tooling and automated validation frameworks to support reliable algorithm deployment into clinical production.\n \n Cross-Functional Collaboration \n \n Partner with Research, Product Development, Laboratory Operations, Engineering, and Quality teams to implement stable, scalable pipelines and support successful productization\n \n Team Leadership \n \n Lead, mentor and hire a high-performing team of bioinformaticians and data scientists, establishing technical quality standards and clear operational ownership.\n Build technical depth within the team to support expanding product roadmaps across Women's Health and Organ Health.\n \n QUALIFICATIONS: \n \n Master of Science or Ph.D. in a quantitative technical discipline (Biostatistics, Bioinformatics, Computer Science, Physics, Applied Mathematics, or equivalent).\n Minimum of 10 years of experience in Data Science or Bioinformatics, with at least 5 years of direct people management experience leading technical teams.\n Hands-on experience architecting AWS cloud infrastructure for data-intensive bioinformatics workloads and NGS pipeline execution.\n Demonstrated ability to identify capability gaps independently, build scalable infrastructure, and drive execution without waiting for formal structure.\n Strong communicator who builds cross-functional alignment across Research, Engineering, and Quality teams through technical clarity, direct engagement, and data-driven reasoning.\n Track record of developing bioinformatics talent and delivering computational pipelines that support commercial product development.\n \n PREFERRED QUALIFICATIONS: \n \n Experience developing software and pipelines within regulated environments (CLIA, FDA, or ISO framework).\n Experience with MLOps frameworks and pipeline tools (MLflow, Nextflow, WDL, Docker).\n Advanced knowledge of statistical inference, machine learning, and genomic data processing.\n \n Compensation \u0026 Total Rewards  \n This range reflects a good-faith estimate of the base pay we reasonably expect to offer at the time of  hire. Final compensation will vary based on experience, qualifications, and internal equity considerations. \n This position is also eligible for additional compensation and benefits through Natera’s robust Total Rewards program, including: \n \n \n Annual performance incentive bonus \n \n Long-term equity awards \n \n Comprehensive health benefits (medical, dental, vision) \n \n 401(k) with company match \n \n Generous paid time off and company holidays \n \n Additional wellness and work-life benefits \n \n \n Compensation Range \n $205,000 — $256,200 USD \n OUR OPPORTUNITY \n Natera™ is a global leader in cell-free DNA (cfDNA) testing, dedicated to oncology, women’s health, and organ health. Our aim is to make personalized genetic testing and diagnostics part of the standard of care to protect health and enable earlier and more targeted interventions that lead to longer, healthier lives.\n The Natera team consists of highly dedicated statisticians, geneticists, doctors, laboratory scientists, business professionals, software engineers and many other professionals from world-class institutions, who care deeply for our work and each other. When you join Natera, you’ll work hard and grow quickly. Working alongside the elite of the industry, you’ll be stretched and challenged, and take pride in being part of a company that is changing the landscape of genetic disease management.\n WHAT WE OFFER \n Competitive Benefits - Employee benefits include comprehensive medical, dental, vision, life and disability plans for eligible employees and their dependents. Additionally, Natera employees and their immediate families receive free testing in addition to fertility care benefits. Other benefits include pregnancy and baby bonding leave, 401k benefits, commuter benefits and much more. We also offer a generous employee referral program!\n For more information, visit www.natera.com .\n Natera is proud to be an Equal O","salary_min":205000,"salary_max":256200,"location":"San Carlos, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["healthcare","mlops","cloud","data-science"],"apply_url":"https://job-boards.greenhouse.io/natera/jobs/6142472004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-12T22:26:46Z","expires_at":"2026-09-29T13:40:54.473689Z","created_at":"2026-08-25T18:29:43.989774Z","updated_at":"2026-08-30T13:40:54.611121Z","company_name":"Natera","company_slug":"natera","company_logo_url":"https://www.google.com/s2/favicons?domain=natera.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e2cc3cda-c6a3-4f53-b1bb-9e02170e612c"},{"id":"b8a8bf92-cf7f-40c2-8149-7016a7c15ff2","company_id":"861968d1-d9f8-4217-9873-ce4b24851abc","title":"Director of Data Science and Bioinformatics","slug":"director-of-data-science-and-bioinformatics-9c51ad6d","description":"This is an exciting opportunity to lead and grow the Data Science and Bioinformatics function supporting Natera's Women's Health and Organ Health product portfolios. In this role, you will build the bioinformatics capability within the Data Science development team, establish scalable AWS cloud infrastructure, and ensure the quality and reproducibility of the genomic algorithms powering our clinical products.\n PRIMARY RESPONSIBILITIES: \n Strategy and Vision \n \n Build and define the Bioinformatics function within the Data Science development team, identify technical tooling gaps, and execute a roadmap to advance genomics-based algorithm development.\n Establish and enforce pipeline and algorithm quality standards, including review processes, validation frameworks, and documentation practices\n \n Infrastructure and Automation \n \n Own and architect scalable AWS-based data science and bioinformatics infrastructure, ensuring quality, reproducibility, and reliable deployment of Next-Generation Sequencing (NGS) algorithms.\n Implement MLOps tooling and automated validation frameworks to support reliable algorithm deployment into clinical production.\n \n Cross-Functional Collaboration \n \n Partner with Research, Product Development, Laboratory Operations, Engineering, and Quality teams to implement stable, scalable pipelines and support successful productization\n \n Team Leadership \n \n Lead, mentor and hire a high-performing team of bioinformaticians and data scientists, establishing technical quality standards and clear operational ownership.\n Build technical depth within the team to support expanding product roadmaps across Women's Health and Organ Health.\n \n QUALIFICATIONS: \n \n Master of Science or Ph.D. in a quantitative technical discipline (Biostatistics, Bioinformatics, Computer Science, Physics, Applied Mathematics, or equivalent).\n Minimum of 10 years of experience in Data Science or Bioinformatics, with at least 5 years of direct people management experience leading technical teams.\n Hands-on experience architecting AWS cloud infrastructure for data-intensive bioinformatics workloads and NGS pipeline execution.\n Demonstrated ability to identify capability gaps independently, build scalable infrastructure, and drive execution without waiting for formal structure.\n Strong communicator who builds cross-functional alignment across Research, Engineering, and Quality teams through technical clarity, direct engagement, and data-driven reasoning.\n Track record of developing bioinformatics talent and delivering computational pipelines that support commercial product development.\n \n PREFERRED QUALIFICATIONS: \n \n Experience developing software and pipelines within regulated environments (CLIA, FDA, or ISO framework).\n Experience with MLOps frameworks and pipeline tools (MLflow, Nextflow, WDL, Docker).\n Advanced knowledge of statistical inference, machine learning, and genomic data processing.\n The pay range is listed and actual compensation packages are based on a wide array of factors unique to each candidate, including but not limited to skill set, years \u0026 depth of experience, certifications and specific office location. This may differ in other locations due to cost of labor considerations.\n Remote USA\n $186,300 — $232,900 USD \n OUR OPPORTUNITY \n Natera™ is a global leader in cell-free DNA (cfDNA) testing, dedicated to oncology, women’s health, and organ health. Our aim is to make personalized genetic testing and diagnostics part of the standard of care to protect health and enable earlier and more targeted interventions that lead to longer, healthier lives.\n The Natera team consists of highly dedicated statisticians, geneticists, doctors, laboratory scientists, business professionals, software engineers and many other professionals from world-class institutions, who care deeply for our work and each other. When you join Natera, you’ll work hard and grow quickly. Working alongside the elite of the industry, you’ll be stretched and challenged, and take pride in being part of a company that is changing the landscape of genetic disease management.\n WHAT WE OFFER \n Competitive Benefits - Employee benefits include comprehensive medical, dental, vision, life and disability plans for eligible employees and their dependents. Additionally, Natera employees and their immediate families receive free testing in addition to fertility care benefits. Other benefits include pregnancy and baby bonding leave, 401k benefits, commuter benefits and much more. We also offer a generous employee referral program!\n For more information, visit www.natera.com .\n Natera is proud to be an Equal Opportunity Employer. We are committed to ensuring a diverse and inclusive workplace environment, and welcome people of different backgrounds, experiences, abilities and perspectives. Inclusive collaboration benefits our employees, our community and our patients, and is critical to our mission of changing the management of disease worldwide.\n All ","salary_min":186300,"salary_max":232900,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"lead","tags":["cloud","healthcare","mlops","data-science"],"apply_url":"https://job-boards.greenhouse.io/natera/jobs/6135539004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-12T22:26:45Z","expires_at":"2026-09-29T13:40:54.361161Z","created_at":"2026-08-25T18:29:43.984963Z","updated_at":"2026-08-30T13:40:54.510623Z","company_name":"Natera","company_slug":"natera","company_logo_url":"https://www.google.com/s2/favicons?domain=natera.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/b8a8bf92-cf7f-40c2-8149-7016a7c15ff2"},{"id":"94ebc3af-9ff8-4906-a27b-88c6fae74950","company_id":"83c597c2-a4b2-4517-99df-1ac8c90756d5","title":"Senior, ML Engineer - 3D Reconstruction","slug":"senior-ml-engineer-3d-reconstruction-e19018d3","description":"About the Company     \n At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business. A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners.   Now a part of the Daimler family , we are focused solely on developing software for automated trucks to transform how the world moves freight.  Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer.  \n Meet The Team:\n The Pseudo-Labeling team's goal is to create high-quality annotations on sensor data (images, point clouds). The annotations include 2D, 3D bounding boxes, classes, trajectories, lane lines, segmentations, depths, and high-definition map elements. The annotations are then used by different downstream users — for example, perception teams use them to train various models, mapping teams use them to build and maintain HD maps, and simulation teams use them for generating new data.\n What You’ll Do:\n \n Design, implement, test and deploy offline 3D reconstruction, lane line detection, and automatic mapping/map creation modules to generate high-quality annotations on Cloud Services from logged sensor data (Cameras, Lidars, Radars, GPS/IMU).\n Build and refine lane line annotation pipelines, applying the latest lane line detection and creation machine learning models to automate and scale map creation.\n Develop and improve pose estimation algorithms to support accurate localization, sensor fusion, and 3D scene reconstruction.\n Demonstrate project management skills, serving as project lead guiding less experienced team members in multiple facets of project execution.\n Stay up to date with the latest developments in AI and ML for autonomous driving, 3D reconstruction, and automated mapping.\n Independently develop offline perception and mapping models or algorithms using disciplined software development processes, making recommendations for developing new code or re-using existing code, implementing version control, and maintaining documentation of created applications.\n Define and implement ingestion, data preparation, curation, and governance of large, multi-faceted data sets supporting analytics models and workflows.\n Proactively assess current capabilities to identify areas for improvement, proposing solutions that align with core strategy and operation.\n Measure and track auto-labeling and map creation quality to meet internal customer requirements.\n Guide and produce information products, supporting visualization and data accessibility in a customer-centric manner.\n Evaluate and make recommendations regarding technical advances that improve productivity and quality, reduce flow times, and enhance operational surety.\n Develop guidelines and standards for analytics and machine learning models, their deployment, and associated processes.\n Provide technical guidance or business process expertise, technical leadership, coaching and mentoring to team members.\n \n What You’ll Need to Succeed:\n \n Considered highly skilled and proficient in discipline; conducts complex, important work under minimal supervision and with wide latitude for independent judgment.\n Scope of Influence: Expected to drive alignment across team interfaces to the rest of the organization. Designs, maintains and owns team technical solutions and drives consensus. Mentors and guides engineers within the group.\n Bachelor’s Degree in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrated competencies and technical proficiencies typically acquired through 6+ years of experience OR; Master’s Degree in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrated competencies and technical proficiencies typically acquired through 3+ years of experience OR; PhD in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrated competencies and technical proficiencies typically acquired through 1+ years of experience.\n Required Qualifications (some combination of the following skills): \n \n Experience in lane line annotation creation or automatic mapping/map creation.\n Familiarity with the latest lane line detection and creation machine learning models.\n Familiarity with pose estimation.\n Active Learning \u0026 Pseudo-labeling – Computer Vision, Deep Learning, Model training.\n Two of the following: 2D/3D Object Detection, Tracking, Sensor Fusion, Semantic Segmentation, SLAM, BEV.\n Scaled ML Operations (MLOps) and Tooling – ML Frameworks, experiment tracking, model registry, MLflow, Weights and Biases, ML Metrics and Evaluation / Quality.\n Distributed machine learning frameworks – PyTorch, Lightning, Ray.\n Model Data Curation – Parquet data processing (PyArrow, Daft, Pandas, etc).\n Development Tools \u0026 Eco-Sys","salary_min":177300,"salary_max":212800,"location":"Ann Arbor, MI","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"senior","tags":["computer-graphics","autonomous-vehicles","robotics","payments","deep-learning","mlops","pytorch","computer-vision"],"apply_url":"https://job-boards.greenhouse.io/torcrobotics/jobs/8640183002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-10T18:30:13Z","expires_at":"2026-09-29T13:36:07.714213Z","created_at":"2026-08-25T18:27:50.524075Z","updated_at":"2026-08-30T13:36:07.85Z","company_name":"Torc Robotics","company_slug":"torc-robotics","company_logo_url":"https://www.google.com/s2/favicons?domain=torc.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/94ebc3af-9ff8-4906-a27b-88c6fae74950"},{"id":"e61dbb1b-e3c5-4cef-83a8-ced80e3e4e2d","company_id":"83c597c2-a4b2-4517-99df-1ac8c90756d5","title":"Senior, Software Engineer - ML Data Delivery","slug":"senior-software-engineer-ml-data-delivery-7d999487","description":"About the Company: \n At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business. A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners. Now a part of the Daimler family, we are focused solely on developing software for automated trucks to transform how the world moves freight. Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer.\n Meet The Team:  \n The Pseudo-Labeling team's goal is to create high-quality annotations on sensor data (images, point clouds). The annotations include 2D, 3D bounding boxes, classes, trajectories, lane lines, segmentations, depths, etc. The annotations are then used by different downstream users — for example, perception teams use them to train various models, and simulation teams use them for generating new data. This role sits at the intersection of that pseudo-labeling data and the online perception team, building the systems that select, package, and deliver the right data to feed on-demand model training.\n What You'll Do: \n \n Design, implement, test and deploy tooling and pipelines for internal quality control and issue identification of pseudo-labeled data, ensuring annotations meet the bar required by downstream model training.\n Provide statistical support and report on pseudo-label quality, coverage, and pipeline health to internal stakeholders and leadership.\n Support secondary data selection (virtual packaging) to curate and feed on-demand data to online model training.\n Support the build-out of the ML data delivery system that enables the online perception team to train models on-demand.\n Drive general pipeline improvement and optimization across the pseudo-labeling and data delivery stack, identifying and resolving bottlenecks in throughput, quality, or reliability.\n Demonstrate project management skills, serving as project lead guiding less experienced team members in multiple facets of project execution.\n Stay up to date with the latest developments in offline perception, data pipeline engineering, and ML data infrastructure for autonomous driving.\n Independently develop tools, services, and algorithms using disciplined software development processes, making recommendations for developing new code or re-using existing code, implementing version control, and maintaining documentation of created applications.\n Define and implement ingestion, data preparation, curation, and governance of large, multi-faceted data sets supporting analytics and ML training workflows.\n Proactively assess current capabilities to identify areas for improvement, proposing solutions that align with core strategy and operation.\n Guide and produce information products, supporting visualization and data accessibility in a customer-centric manner.\n Evaluate and make recommendations regarding technical advances that improve productivity and quality, reduce flow times, and enhance operational surety.\n Develop guidelines and standards for data quality control, data delivery systems, and their deployment, and associated processes.\n Provide technical guidance or business process expertise, technical leadership, coaching and mentoring to team members.\n \n What You'll Need to Succeed:  \n \n Considered highly skilled and proficient in discipline; conducts complex, important work under minimal supervision and with wide latitude for independent judgment.\n Scope of Influence: Expected to drive alignment across team interfaces to the rest of the organization. Designs, maintains and owns team technical solutions and drives consensus. Mentors and guides engineers within the group.\n Bachelor’s Degree in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrated competencies and technical proficiencies typically acquired through 6+ years of experience OR;\n Master’s Degree in Computer Science, Robotics, Electrical Engineering or related technical field plus demonstrated competencies and technical proficiencies typically acquired through 3+ years of experience OR;\n Required Qualifications (some combination of the following skills): \n \n Familiarity with the offline perception stack in general, and knowledge of how pseudo-label data is produced and related best practices.\n Strong software engineering background building and operating data pipelines and services at scale.\n Statistical analysis and reporting skills, with the ability to translate data quality findings into actionable insights.\n Scaled ML Operations (MLOps) and Tooling – ML Frameworks, experiment tracking, model registry, MLflow, Weights and Biases, ML Metrics and Evaluation / Quality.\n Model Data Curation – Parquet data processing (PyArrow, Daft, Pandas, etc).\n Development Tools \u0026 Eco-System (at scale) – Proficiency in Python sof","salary_min":160800,"salary_max":193000,"location":"Ann Arbor, MI","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"senior","tags":["payments","data-pipeline","mlops","pytorch","autonomous-vehicles","robotics"],"apply_url":"https://job-boards.greenhouse.io/torcrobotics/jobs/8634727002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-10T18:22:54Z","expires_at":"2026-09-29T13:36:08.092463Z","created_at":"2026-08-25T18:27:50.564939Z","updated_at":"2026-08-30T13:36:08.228172Z","company_name":"Torc Robotics","company_slug":"torc-robotics","company_logo_url":"https://www.google.com/s2/favicons?domain=torc.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e61dbb1b-e3c5-4cef-83a8-ced80e3e4e2d"},{"id":"96c70bdf-9099-40eb-a750-add6fecebc98","company_id":"6734f15a-40ed-4186-ae4a-d774c655ae58","title":"Senior Software Engineer I/II, Back-end/Data, Robotics","slug":"senior-software-engineer-iii-back-enddata-robotics-669bb616","description":"Your Impact at LILA \n You will build the back-end and data foundations of the systems that schedule work across our autonomous labs, model them as digital twins, and turn raw experimental output into trustworthy, queryable data. In Robotics, you'll own core services scientists, operators, and engineers rely on daily — the ones that decide how efficiently our robots run and how much our scientists trust their data.\n What You'll Be Building \n \n Design scheduling systems: allocate experiments, instruments, and compute across autonomous labs, modeling long-running jobs as durable Temporal workflows.\n Model digital twins: stand up the simulation/digital-twin data layer so teams predict factory behavior before committing resources.\n Own technical data management: build the Flyte pipelines, S3 lakehouse, and PostgreSQL models that turn instrument output into governed, trustworthy data.\n Enable data science: ship batch and event-driven pipelines that put clean data in the hands of scientists and models.\n Deliver capacity planning: create customer-facing services that help teams forecast factory capacity.\n Operate for scale: run services on AWS EKS with Docker/ECR, Terraform, and GitHub Actions, reliable as the factory grows.\n \n What You'll Need to Succeed \n \n 4–8 years of back-end engineering: production services in Python (FastAPI) at scale.\n Data engineering: pipelines on a workflow orchestrator such as Flyte, Temporal, or similar.\n Data modeling across stores: hands-on SQL (PostgreSQL), object storage (S3), and data lakehouse architectures.\n Cloud-native delivery: a major cloud with containers, infrastructure as code, and CI/CD — ideally AWS EKS (Docker/ECR), Terraform, and GitHub Actions.\n End-to-end ownership: taking services from design through delivery, reliability, and iteration.\n Clear collaboration: communicating technical tradeoffs plainly with engineers, scientists, and business partners.\n \n Bonus Points For \n \n Systems engineering in regulated industries: systems-thinking rigor in safety- or quality-critical settings — aerospace, manufacturing, or healthcare/life sciences. Exposure to 21 CFR Part 11, ALCOA+, GAMP 5 / FDA CSA, or ISA-95 a plus.\n Domain exposure: robotics, lab automation, LIMS, or manufacturing/MES environments.\n Scheduling \u0026 optimization: job scheduling, resource allocation, or constraint/optimization problems.\n Data-science enablement: distributed compute (Ray), experiment tracking (Weights \u0026 Biases), or model serving.\n Event-driven messaging: NATS, MQTT, or similar pub/sub for high-volume telemetry.\n Compensation \n We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.\n U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.\n International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.\n Expected Base Salary Range\n $144,000 — $240,000 USD \n About LILA \n Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.\n LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.\n Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.\n We’re All In \n Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.\n Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy .\n A Note to Agencies \n Lila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s inte","salary_min":144000,"salary_max":240000,"location":"Boston, MA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["data-pipeline","healthcare","robotics","cloud","mlops","backend"],"apply_url":"https://job-boards.greenhouse.io/lilasciences/jobs/4339324009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-10T16:38:32Z","expires_at":"2026-09-29T13:48:26.684286Z","created_at":"2026-08-25T18:33:17.099953Z","updated_at":"2026-08-30T13:48:26.832676Z","company_name":"Lila Sciences","company_slug":"lila-sciences","company_logo_url":"https://www.google.com/s2/favicons?domain=lila.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/96c70bdf-9099-40eb-a750-add6fecebc98"},{"id":"04392269-370a-46e6-b81f-775cc53ce0fd","company_id":"12105b3e-eb1d-4a92-95b6-855042facaf1","title":"AI Security Architect","slug":"ai-security-architect-6664fb8b","description":"At Qualtrics, we create software the world’s best brands use to deliver exceptional frontline experiences, build high-performing teams, and design products people love. But we are more than a platform—we are the creators and stewards of the Experience Management category serving over 18K clients globally. Building a category takes grit, determination, and a disdain for convention—but most of all it requires close-knit, high-functioning teams with an unwavering dedication to serving our customers. When you join one of our teams, you’ll be part of a nimble group that’s empowered to set aggressive goals and move fast to achieve them. Strategic risks are encouraged and complex problems are solved together, by passing the mic and iterating until the best solution comes to light. You won’t have to look to find growth opportunities—ready or not, they’ll find you. From retail to government to healthcare, we’re on a mission to bring humanity, connection, and empathy back to business. Join over 5,000 people across the globe who think that’s work worth doing.\n  \n AI Security Architect \n Why We Have This Role \n AI systems face threats that traditional security architecture wasn't built for: adversarial manipulation, model theft, data poisoning, prompt injection, and supply-chain risk across the ML pipeline. The AI Security Architect leads the design and implementation of security frameworks that protect our AI systems — building resilience against these threats and earning our customers' trust.\n How You’ll Find Success \n \n \n Strategic Visionary: You hold a holistic view of AI/ML threat models, proactively identifying and mitigating emerging risks. You guide security architecture strategy so it aligns with the organization’s risk posture and business goals.\n \n Technical Innovator: Your drive for continuous improvement pushes you to explore and implement cutting-edge AI security practices — adversarial robustness testing, model hardening, secure MLOps — keeping our AI systems resilient against evolving threats.\n \n Collaborative Leader: You build partnerships across security, engineering, and data science, promoting best practices in AI security. Your ability to foster collaboration creates an informed, cohesive environment dedicated to protecting our systems.\n \n Resilient and Adaptive: You thrive in a fast-paced environment, managing complex projects while guiding cross-functional teams through shifts in the threat landscape or technology. Your problem- solving skills are essential to navigating the complexities of AI security governance.\n \n How You’ll Grow \n \n \n Shape Industry Standards: Participate in industry conferences, thought leadership forums, and professional organizations to influence the future of AI security practices.\n \n Executive Presence: Increase your visibility and involvement in executive-level discussions, refining how you communicate strategic security insights.\n \n Expand Your Leadership Toolkit: As a thought leader in AI security architecture, mentor and coach emerging talent, growing the security expertise across the organization.\n \n Complex Problem-Solving: Tackle significant AI security challenges that sharpen your analytical,critical thinking, and strategic planning abilities.\n \n Things You’ll Do \n \n Drive Innovation in AI Security: Lead research, evaluation, and implementation of next-generation security technologies and methodologies — adversarial defense, model security, secure MLOps pipelines — that support our AI solutions.\n Conduct Security Reviews: Perform security reviews of AI products and proposed designs, including architectures built on Model Context Protocol (MCP) and agentic AI systems, identifying risks such as tool-use abuse, unauthorized action-taking, and insecure agent-to-agent or agent to-tool communication.\n Develop Standards \u0026 Reference Architectures: Author AI-related technical security standards, guardrails, and reference architectures that give product and engineering teams a secure, repeatable blueprint for building and deploying AI systems.\n Shape Security Strategy: Develop a comprehensive AI security strategy that aligns with the organization’s risk profile and business objectives.\n Manage Complex Projects: Oversee execution of large-scale AI security initiatives, ensuring on-time, on-budget delivery while proactively addressing risks and fostering adaptability within teams.\n Foster a Culture of Excellence: Create an environment that promotes knowledge sharing, collaboration, and continuous learning. Mentor colleagues to build a high-performing team committed to our security objectives.\n \n What We’re Looking For On Your Resume \n \n While we value the wealth of experience, we put more emphasis on your capability and the outcome you’ve produced. For this role, these elements are particularly important:\n Extensive Architectural Expertise: 5+ years of experience in AI/ML security, cybersecurity architecture, or a rela","salary_min":168000,"salary_max":221000,"location":"Seattle, WA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","healthcare","mlops","security"],"apply_url":"https://www.qualtrics.com/careers/us/en/job/8108875?gh_jid=8108875","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-07T16:19:45Z","expires_at":"2026-09-29T13:49:13.217826Z","created_at":"2026-08-25T18:33:38.578504Z","updated_at":"2026-08-30T13:49:13.349914Z","company_name":"Qualtrics","company_slug":"qualtrics","company_logo_url":"https://www.google.com/s2/favicons?domain=qualtrics.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/04392269-370a-46e6-b81f-775cc53ce0fd"},{"id":"bb94a3fb-6dfc-46df-8277-79fa4aececff","company_id":"7455e78c-482b-4f7b-9d62-11b78645818a","title":"Software Engineer II, Model Platform","slug":"software-engineer-ii-model-platform-1d0a7ce5","description":"About the Role \n Abnormal AI is looking for a Software Engineer II to join the Detection Team. The Detection Division is focused on building the world’s most advanced technology to identify and stop email and cloud-based attacks that were previously undetectable, helping make the world a safer place. As a Software Engineer focused on building systems for Detection’s Model Platform, you will be responsible for making feature development at Abnormal a fast, responsive, stable, and confident experience for our ML and Data Science teams.\n The ideal candidate brings a first-principles approach to building scalable, customer-centric solutions, an ownership-oriented mindset, and the ability to iterate quickly and autonomously on novel problems.\n What You Will Do \n \n Leverage industry-standard AI tools to architect, design, build, deploy, and maintain Model Serving infrastructure that supports a world-class Detection Engine.\n Own projects that scale our model serving and data processing services to handle 10x the traffic we serve today.\n Own real-time and near real-time streaming pipelines, and online feature serving services.\n Collaborate closely with MLE and Data Science teams by distilling feedback, correlating it to strategy, and executing.\n \n Must Haves \n \n 4+ years of experience as a Software Engineer or in a similar role, with hands-on experience building data-focused solutions.\n Proficiency leveraging AI tools to accelerate engineering outcomes through discovery, design, implementation, and rollout of software systems.\n Experience maintaining large-scale distributed systems on cloud platforms such as AWS, GCP, or Azure, including a strong grasp of best practices in cloud-based engineering.\n Experience with real-time and near real-time data pipelines or streaming services.\n Strong fundamentals in computer science, data structures, and performance optimization.\n BS degree in Computer Science, Applied Sciences, Information Systems, or other related engineering field.\n \n Nice to Have \n \n Familiarity with our stack: AWS, Kubernetes, Python/Django, Golang, and Postgres.\n Experience building scalable, enterprise-grade applications.\n Experience with web security (e.g., OWASP Top 10).\n Familiarity with AI development tools such as Cursor, GitHub Copilot, or Claude.\n \n #LI-PP1\n Actual compensation will be determined based on several non-discriminatory factors including skills, experience, qualifications, and geographic location. In addition to base salary, this role may be eligible for bonus or incentive compensation, equity, and a comprehensive benefits package.\n Base salary range:\n $149,200 — $214,500 USD \n A note on AI in our process:  Abnormal AI uses AI-assisted tools to help our recruiting team prepare for candidate interviews. These tools analyze resume content and role requirements to suggest interview questions and areas for the interviewer to explore.They do not make hiring decisions or screen candidates automatically. Every decision about a candidacy is made by a person. Further, if your application is successful and Abnormal AI makes a conditional offer of employment, we will carry out pre-employment checks which must be successfully completed to progress to a final offer. All processes and pre-employment checks are in line with prevailing legislation and Abnormal AI's policies relevant to our security and privacy standards.\n Abnormal AI is an equal opportunity employer. Qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability, protected veteran status or other characteristics protected by law. For our EEO policy statement please click here . If you would like more information on your EEO rights under the law, please  click here .","salary_min":149200,"salary_max":214500,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"mid","tags":["distributed-systems","code-generation","data-pipeline","mlops"],"apply_url":"https://abnormal.ai/careers/jobs/7829979003?gh_jid=7829979003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-07T14:15:09Z","expires_at":"2026-09-29T13:34:23.230252Z","created_at":"2026-08-25T18:27:11.645653Z","updated_at":"2026-08-30T13:34:23.370035Z","company_name":"Abnormal Security","company_slug":"abnormal-security","company_logo_url":"https://www.google.com/s2/favicons?domain=abnormalsecurity.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/bb94a3fb-6dfc-46df-8277-79fa4aececff"}],"page":1,"per_page":20,"total":460,"total_is_exact":true,"total_pages":23}