{"access":{"catalog_url":"https://aidevboard.com/api/v1/catalog","description":"Public read endpoints are open and free. API keys are optional for stable agent identity and keyed hourly throttling.","docs_url":"https://aidevboard.com/docs","employer_pilot_url":"https://aidevboard.com/verified-interview-pilot","mode":"open","register_url":"https://aidevboard.com/api/v1/register"},"candidate_resume_action":{"application_authorized":false,"candidate_charge":0,"endpoint":"https://aidevboard.com/api/v1/candidate/resume-preview","job_id_json_path":"jobs[].id","method":"POST","preview_requires_identity":false,"required_body_fields":["job_id","evidence_bullets"],"requires_explicit_human_review":true,"saved_artifact_protocol":"mcp","saved_artifact_requires_verified_human":true,"saved_artifact_tool":"compile_job_specific_resume","search_requires_identity":false,"status":"available_after_candidate_selects_job","submission_performed":false,"uses_candidate_verified_evidence":true},"degraded":false,"estimated":false,"has_next":true,"jobs":[{"id":"a60887bd-18b6-4819-b8ac-a8ce688f7d3f","company_id":"a0000000-0000-0000-0000-000000000003","title":"Machine Learning Research Scientist, Evaluations","slug":"machine-learning-research-scientist-evaluations-47ca5c35","description":"Scale works with the industry's leading AI labs to provide high quality data and accelerate progress in GenAI research. We are looking for Research Scientists and Research Engineers with expertise in LLM post-training (SFT, RLHF, reward modeling) and evaluation. This role is on the evaluation pod within the GenAI Research Organization and will focus on building benchmarks and diagnosing model failure modes in both text and multimodal modalities.\n In this role, you will develop rigorous evaluations and diagnostic methods that reveal where frontier models fail and why. You will collaborate with researchers and engineers to define best practices in evaluation-driven AI development. You will also partner with top foundation model labs to translate failure analysis into technical and strategic input on the next generation of generative AI models.\n You will: \n \n Analyze model behavior to identify, characterize, and diagnose failure modes in frontier LLMs and Agents.  You’ll identify everything from capability gaps and reasoning errors to robustness and alignment issues, all focusing on RCA.\n Design and build benchmarks and evaluation methods that measure LLM capabilities in both text and multimodal modalities.\n Apply post-training expertise (SFT, RLHF, reward modeling) to connect observed failures to the data and training interventions that address them.\n Publish research findings in top-tier AI conferences.\n \n Ideally you’d have: \n \n Ph.D. or Master's degree in Computer Science, Machine Learning, AI, or a related field.\n Deep understanding of deep learning, reinforcement learning, and large-scale model fine-tuning.\n Experience with post-training techniques such as RLHF, preference modeling, or instruction tuning, and with LLM evaluation or benchmark development.\n Excellent written and verbal communication skills.\n Published research in areas of machine learning at major conferences (NeurIPS, ICML, ICLR, ACL, EMNLP, CVPR, etc.) and/or journals.\n Previous experience in a customer facing role.\n Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend. \n Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York, Seattle is:\n $180,600 — $225,750 USD \n PLEASE NOTE:  Our policy requires a 90-day waiting period before reconsidering candidates for the same role. This allows us to ensure a fair and thorough evaluation of all applicants. \n About Us: \n At Scale, our mission is to develop reliable AI systems for the world's most important decisions. Our products provide the high-quality data and full-stack technologies that power the world's leading models, and help enterprises and governments build, deploy, and oversee AI applications that deliver real impact. We work closely with industry leaders like Meta, Ernst \u0026 Young, Mayo Clinic, Time Inc., the Government of Qatar, and U.S. government agencies including the Army and Air Force. We are expanding our team to accelerate the development of AI applications. \n We believe that everyone should be able to bring their whole selves to work, which is why we are proud to be an inclusive and equal opportunity workplace. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability status, gender identity or Veteran status.  \n We are committed to working with and providing reasonable accommodations to applicants with physical and mental disabilities. If you need assistance and/or a reasonable accommodation in the application or recruiting process due to a disability, please contact us at accommodations@scale.com. Please see the United States Department of Labor's Know Your Rights poster for additional information. \n We comply with the United States Department of Labor's Pay Transparency provision .  \n PLEASE NOTE: We co","salary_min":180600,"salary_max":225750,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["deep-learning","fine-tuning","generative-ai","reinforcement-learning","search","nlp","llm","evaluation"],"apply_url":"https://job-boards.greenhouse.io/scaleai/jobs/4728014005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-26T18:51:42Z","expires_at":"2026-09-29T13:31:40.355249Z","created_at":"2026-08-27T13:31:38.7307Z","updated_at":"2026-08-30T13:31:40.501539Z","company_name":"Scale AI","company_slug":"scale-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=scale.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a60887bd-18b6-4819-b8ac-a8ce688f7d3f"},{"id":"9a42e317-7954-4330-9e7d-59cad3214bb9","company_id":"8115806a-6d9c-48b9-9a92-5eb180bbd1ef","title":"Applied Research Scientist, AI Research","slug":"applied-research-scientist-ai-research-428d0a90","description":"Descript's Research team builds the models behind the product's most distinctive features: Video Regenerate and lipsync, video translation, zero-shot voice and roomtone cloning, and Studio Sound. We don't build general-purpose generative models. We pick specific problems in the editing workflow and build specialized models for them. This isn't research for its own sake. Everything we build is meant to ship, and most of it has, going from prototype to a production feature used by millions of creators within months.\n This role is focused on multimodal understanding: training models to perceive edited media the way a human video editor does. Underlord, our AI editing agent, reasons about a project largely through a textual representation of it. Giving it direct perception of the media it's working on is what will let it judge its own output and reason about the creative choices in an edit, not just the structure of a project. It's also an open research problem, since there's no settled way to represent or evaluate editorial craft, whether a cut lands or whether the pacing works. We have a unique dataset to work with.\n Some recent work from the team:\n \n Audio editing by latent inpainting : regenerating a masked span of speech \n Video Regenerate : regenerating a speaker's lower face to match new or translated audio\n Jumpcut Smoothing : generating a bridge across a cut so the join plays like a continuous take\n Anchored Tree Sampling : tree-based imputation that bounds drift in long video generation\n PoDAR : disentangling power from semantics in audio latents to make them easier to model\n \n More at descript.com/research .\n What you'll do\n \n Multimodal understanding: build vision-language systems that let Descript's agentic editing features reason over the visual and audio content of a project.\n Evaluation: design the benchmarks and evals that make editorial quality measurable, and that balance quality against cost and latency.\n Data: build the datasets your work depends on, including synthetic data generation where real examples don't exist at scale.\n Training: train specialized models from scratch or fine-tune existing foundation models, whichever gets the capability we need.\n Shipping: take models from prototype to production with the agent and engineering teams.\n Direction-setting: identify the next research direction that should become a Descript feature, not just a paper. More senior candidates should expect to own this directly; more junior candidates will grow into it.\n Publishing: take your work to academic venues if you'd like. We support it, but it isn't a requirement of the role.\n \n What you bring\n Required\n \n Proven ability to design and implement deep learning algorithms, demonstrated by publications, open-source work, or models you've shipped.\n Strong programming skills and deep fluency in PyTorch.\n A track record of generating new ideas in machine learning. You produce more ideas than you can implement, and once an experiment setup is established, you can run and evaluate many of them quickly rather than being bottlenecked on infrastructure.\n Strong experimental judgment. You test ideas fast, and you're honest with yourself and the team about which ones don't pan out.\n Clear written and verbal communication, including when a direction isn't working, so the team doesn't waste time following a lead that's already dead.\n A PhD or Master's in deep learning or a related field, or equivalent experience. We care about the track record more than the credential.\n \n At least one of the following must be true:\n \n Lead or first author of an accepted publication in a top venue: CVPR, ICCV, ECCV, NeurIPS, ICML, ICLR, or similar.\n Played a key role in shipping a production feature with deep learning as a core component.\n \n More senior candidates (Senior and Staff) should also bring a track record of owning research direction rather than executing a plan handed to them, and experience mentoring or technically leading other researchers or engineers.\n Where breadth helps\n Direct experience in multimodal understanding is welcome but not required, and we don't require domain-specific expertise in computer vision or speech and audio. Our team spans both, and strong general deep learning ability transfers. We hire against the bar above, and then expect you to grow into the domain. Depth in any of these is a strong signal:\n \n Vision-language models and multimodal understanding.\n Generative modeling for video, audio, or images.\n Post-training, fine-tuning, and RL on large foundation models.\n Building evaluation systems for generative or agentic outputs where metrics resist clean definitions.\n Taking a research idea through to a shipped, production-facing feature.\n \n Compensation and benefits\n Base salary range: $197,000–$262,500, plus equity and benefits. Final offer amounts will carefully consider multiple factors, including prior experience, expertise, location, and level, and may vary from the amount above.\n  \n IMPO","salary_min":197000,"salary_max":262500,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["generative-ai","deep-learning","fine-tuning","computer-vision","pytorch","healthcare","agents","research"],"apply_url":"https://boards.greenhouse.io/descript/jobs/7967440003?gh_jid=7967440003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-25T00:18:24Z","expires_at":"2026-09-29T13:35:54.489992Z","created_at":"2026-08-25T18:27:44.358668Z","updated_at":"2026-08-30T13:35:54.624927Z","company_name":"Descript","company_slug":"descript","company_logo_url":"https://www.google.com/s2/favicons?domain=descript.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/9a42e317-7954-4330-9e7d-59cad3214bb9"},{"id":"241f8621-b66a-4668-ae4c-953914e72085","company_id":"b467c425-56b3-40ce-826a-e603e82a08bd","title":"Senior Software Engineer - Content Understanding","slug":"senior-software-engineer-content-understanding-ab972d21","description":"Every day, tens of millions of people come to Roblox to explore, create, play, learn, and connect with friends in 3D immersive digital experiences– all created by our global community of developers and creators.  \n At Roblox, we’re building the tools and platform that empower our community to bring any experience that they can imagine to life. Our vision is to reimagine the way people come together, from anywhere in the world, and on any device. We’re on a mission to connect a billion people with optimism and civility, and looking for amazing talent to help us get there.  \n A career at Roblox means you’ll be working to shape the future of human interaction, solving unique technical challenges at scale, and helping to create safer, more civil shared experiences for everyone. \n As a Senior Software Engineer within the Creator Organization, you will develop innovative full-stack solutions that define the future of Roblox’s Content Understanding Platform. This platform processes billions of pieces of content—spanning 3D models, audio files, text, video, and entire experiences—extracting structured information about their meaning, context, and relationships. The Content Understanding Team develops cutting-edge AI models, advanced computer vision systems, and highly scalable backend platforms to power search, discovery, and moderation across Roblox. Your contributions will enable seamless asset discovery, automate moderation at scale, and drive transformative generative AI tools that reshape how millions of creators and users engage with Roblox.\n You Will: \n \n Solve full-stack challenges to improve how AI, creators, and users describe and get along with content, including images, 3D models, audio, text, and video.\n Craft and build scalable pipelines for training, evaluating, and deploying machine learning models to support content annotation and discovery.\n Develop robust backend systems to power real-time search, discovery, and powerful generative AI features.\n Blend innovation with practicality, applying the latest AI research to build impactful, production-ready solutions.\n Collaborate with engineers, product managers, and multi-functional teams to deliver bold technical projects.\n \n You Have: \n \n 7 years of strong programming skills in at least two languages (Python, C#, C++, Java) and a willingness to learn others as needed.\n Exposure to front end technologies and frameworks such as React or Angular.\n Practical experience crafting and scaling backend systems in cloud environments.\n Confirmed expertise across the stack, including backend development to deploying ML models in production environments.\n Familiarity with image or 3D object understanding, ideally within gaming or content creation industries.\n A “get stuff done” mentality with a readiness to solve challenges and take on tasks beyond your comfort zone to get results.\n A great foundation in computer vision, AI, or related fields.\n \n You Are \n \n Experience with large-scale search systems, generative AI, or semantic content understanding.\n Experience with modern deep learning frameworks (e.g., PyTorch, TensorFlow) and end-to-end ML workflows.\n Knowledge of 3D geometry or asset workflows.\n Passion for empowering creators through innovative technology.\n For roles that are based at our headquarters in San Mateo, CA: The starting base pay for this position is as shown below. The actual base pay is dependent upon a variety of job-related factors such as professional background, training, work experience, location, business needs and market demand. Therefore, in some circumstances, the actual salary could fall outside of this expected range. This pay range is subject to change and may be modified in the future. All full-time employees are also eligible for equity compensation and for benefits as described on this page .\n Annual Salary Range\n $243,290 — $295,250 USD \n Roles that are based in an office are onsite Tuesday, Wednesday, and Thursday, with optional presence on Monday and Friday (unless otherwise noted).\n Roblox provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws. Roblox also provides reasonable accommodations to candidates with qualifying disabilities or religious beliefs during the recruiting process.\n For US based roles only, please note the Company may not be able to employ candidates for this role who have United States work authorization related to certain U.S. visa categories, or support future H-1B sponsorship at this time.","salary_min":243290,"salary_max":295250,"location":"San Mateo, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["computer-vision","generative-ai","pytorch","deep-learning","tensorflow"],"apply_url":"https://careers.roblox.com/jobs/8094470?gh_jid=8094470","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-24T22:09:19Z","expires_at":"2026-09-29T13:47:50.815075Z","created_at":"2026-08-25T18:33:06.219084Z","updated_at":"2026-08-30T13:47:50.942237Z","company_name":"Roblox","company_slug":"roblox","company_logo_url":"https://www.google.com/s2/favicons?domain=roblox.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/241f8621-b66a-4668-ae4c-953914e72085"},{"id":"41d74cb8-e5eb-40fa-8396-d41068ec80f1","company_id":"e8c9f3a5-9310-43f5-9341-321fe6d93a92","title":"Automotive System Engineer","slug":"automotive-system-engineer-eb519350","description":"About us    \n Founded in 2017, Wayve is the leading developer of Embodied AI technology.  Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems.\n Our vision is to create autonomy that propels the world forward.  Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving.  In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future.\n At Wayve, your contributions matter.  We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact.  \n Make Wayve the experience that defines your career!  \n The role  \n As automotive system engineer, you will be responsible for designing the L2/L3 automotive grade system based on our AV2.0 approach. This includes the strategy and the concept of how to embed our fundamentally new autonomy solution in future OEM vehicle infrastructures. You will also engage with our customers and represent our approach and our company in the planning of collaboration projects. \n You will grow with the company and be critical in establishing how we design our system closely with software engineering leads. Our stack includes base software for sensor data processing, neural network execution and diagnostics and robotic functions for vehicle control. \n You will also act as a coach to others towards an automotive grade, requirement based development mindset to help teams deliver automotive grade software. \n Key responsibilities: \n Requirement Definition: \n \n \n Define clear, actionable system requirements for assigned products, whether focused on L2+, L3 automated driving, or ADAS functionalities.\n Collaborate with product managers, software developers, and hardware teams to ensure requirements align with customer and business needs.\n Ensure traceability and alignment of requirements throughout the product lifecycle.\n \n Data Driven Development:  \n \n Create comprehensive scenarios that the system must handle, ensuring it performs reliably in various real-world conditions. Support for selecting and creating such testing scenarios from data corpus and for ensuring that data-driven insights guide system development and testing processes.\n Ensures that scenario definitions are validated and refined through collaboration with multiple teams, aligning with overall business and customer needs.\n \n Support Implementation: \n \n Work closely with software developers to align on data collection requirements, scenarios, KPIs and behaviour specifications\n Work closely with the solutions and architecture teams to develop the system architecture based on the system requirements and customer constraints.\n Oversee and support the integration of solutions into vehicles, ensuring seamless functionality and compatibility.\n \n Hands-On Testing: \n \n Actively test and validate systems in vehicles to gain direct experience and provide firsthand feedback.\n Analyze performance in real-world scenarios, identifying strengths and areas for improvement.\n Define test specifications at the system-level (working with the HIL team) and the vehicle-level integration.\n \n Problem Resolution: \n \n Serve as the first line of support for problem resolution, diagnosing and addressing system issues.\n Collaborate across teams to escalate and resolve complex issues, ensuring timely solutions.\n \n Continuous Improvement: \n \n Provide input on process enhancements and system optimizations based on testing and user feedback.\n Contribute to the continuous evolution of Wayve’s L2+/L3 and ADAS technologies.\n \n \n About you   \n In order to set you up for success as a Systems Engineer at Wayve, we’re looking for the following skills and experience.  \n Essential \n \n Experience: \n \n 5+ years of experience in system engineering, automotive development, or related fields.\n Proven experience in defining and managing requirements for complex systems, preferably in ADAS or automated driving domains.\n Proficiency in requirements management (e.g., Jama, DOORS) and system engineering tools (e.g., MATLAB/Simulink, Enterprise Architect)\n \n Technical Skills: \n \n Strong understanding of automotive systems, including sensors (cameras, radars, etc.), software integration, and vehicle dynamics.\n Experience with system validation and troubleshooting in real-world conditions.\n \n Soft Skills: \n \n Excellent problem-solving skills, with the ability to diagnose and resolve issues effectively.\n Strong communication and collaboration skills, with a proactive and hands-on approach.\n A forward-thinking mindset and experience in implementing disruptive te","salary_min":209700,"salary_max":311400,"location":"Sunnyvale, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["deep-learning","generative-ai","robotics","autonomous-vehicles"],"apply_url":"https://wayve.firststage.co/jobs?gh_jid=8734183002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-21T02:48:47Z","expires_at":"2026-09-29T13:43:21.57688Z","created_at":"2026-08-25T18:31:14.125213Z","updated_at":"2026-08-30T13:43:21.712181Z","company_name":"Wayve","company_slug":"wayve","company_logo_url":"https://www.google.com/s2/favicons?domain=wayve.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/41d74cb8-e5eb-40fa-8396-d41068ec80f1"},{"id":"ac071719-be76-48b4-a476-7a614bc11915","company_id":"2ca4efa5-edc2-4352-a597-ea27086e1e5b","title":"Machine Learning Engineer II, Ads - Response Prediction","slug":"machine-learning-engineer-ii-ads-response-prediction-3e47bda7","description":"We're transforming the grocery industry \n At Instacart, we invite the world to share love through food because we believe everyone should have access to the food they love and more time to enjoy it together. Where others see a simple need for grocery delivery, we see exciting complexity and endless opportunity to serve the varied needs of our community. We work to deliver an essential service that customers rely on to get their groceries and household goods, while also offering safe and flexible earnings opportunities to Instacart Personal Shoppers. \n Instacart has become a lifeline for millions of people, and we’re building the team to help push our shopping cart forward. If you’re ready to do the best work of your life, come join our table.\n Instacart is a Flex First team \n There’s no one-size fits all approach to how we do our best work. Our employees have the flexibility to choose where they do their best work—whether it’s from home, an office, or your favorite coffee shop—while staying connected and building community through regular in-person events. Learn more about our flexible approach to where we work. \n Overview \n About the Role: \n As a Machine Learning Engineer, you will have the opportunity to collaborate closely with senior engineers and product leaders as part of your team. Together, you'll develop and enhance Instacart's ads systems. You will use machine learning to devise and refine solutions in crucial areas such as ads selection, ranking, bidding, and auction across all of Instacart’s consumer facing surfaces and Ads Ecosystems. You will actively contribute to initiatives, assisting in all stages of ML projects from the initial concept, through prototyping and experimentation, to final launch.\n About the Team: \n The Ads Response Prediction team owns systems, algorithms and ML models to ensure a relevant and engaging Ads experience to customers of all the platforms powered by Instacart. This includes search and exploration retrieval systems, Sequential Modeling and Generative Retrieval systems, LLM integrations, relevance models, pCTR models, bidding models and Foundation Models. The team optimizes for an efficient marketplace to ensure customers see ads that help them try new products/brands, advertisers boost their product sales for a good return on investment, and instacart generates the deserving revenue as well.\n  \n About the Job: \n \n Design, develop, and deploy machine learning solutions including data pipelines, model architectures and serving integrations to tackle practical challenges in the ads organization.\n Formulate and scope ambiguous modeling problems from first principles. Translate business observations (e.g., miss-calibration patterns, cold-start underperformance) into well-defined ML research directions with clear evaluation criteria.\n Collaborate closely with product managers, data scientists, and infrastructure engineers to deeply understand business needs and create impactful ML applications.\n Push the envelope on our operational efficiency by continually refining and advancing our algorithms and models.\n Publish and present findings internally. Contribute to the team’s culture of technical rigor through design reviews, paper sharing, and experiment retrospectives.\n \n  \n About You: \n Minimum Qualifications: \n \n Have a graduate degree (masters or PhD) in machine learning, statistics, computer science, information retrieval, or a closely related quantitative field.\n Have strong programming skills and fluency in data manipulation (SQL, Spark, Pandas) and Machine Learning (classical ML and Deep Learning) tools.\n Have strong analytical skills and problem-solving ability.\n Are a strong communicator who can collaborate with diverse stakeholders across all levels.\n \n  \n Preferred Qualifications: \n \n Have 1-2 years of industry experience using machine learning to solve real-world problems with large datasets.\n Knowledge of sequential modeling, Transformer architecture and Foundation Model.\n Familiarity with LLM integrations, agentic workflow and productivity tooling.\n Experience in building large scale online recommendation systems.\n \n #LI-Remote \n Instacart provides highly market-competitive compensation and benefits in each location where our employees work. This role is remote and the base pay range for a successful candidate is dependent on their permanent work location. Please review our Flex First remote work policy here . Currently, we are only hiring in the following provinces: Ontario, Alberta, British Columbia, and Nova Scotia.\n Offers may vary based on many factors, such as candidate experience and skills required for the role. Additionally, this role is eligible for a new hire equity grant as well as annual refresh grants. Please read more about our benefits offerings here .\n For Canadian based candidates, the base pay ranges for a successful candidate are listed below.\n CAN\n $154,000 — $162,500 CAD","salary_min":154000,"salary_max":162500,"location":"Remote (Canada)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"junior","tags":["agents","generative-ai","data-pipeline","fine-tuning","search","llm","deep-learning","machine-learning"],"apply_url":"https://instacart.careers/job/?gh_jid=8143263","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-20T19:14:19Z","expires_at":"2026-09-29T13:39:09.256769Z","created_at":"2026-08-25T18:28:59.889259Z","updated_at":"2026-08-30T13:39:09.402003Z","company_name":"Instacart","company_slug":"instacart","company_logo_url":"https://www.google.com/s2/favicons?domain=www.instacart.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/ac071719-be76-48b4-a476-7a614bc11915"},{"id":"0dfb8aab-f030-4de8-ae57-e7eae9cdd25c","company_id":"e8c9f3a5-9310-43f5-9341-321fe6d93a92","title":"Senior Machine Learning Engineer, Vision Models","slug":"senior-machine-learning-engineer-vision-models-b67985dc","description":"About us    \n Founded in 2017, Wayve is the leading developer of Embodied AI technology.  Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems.\n Our vision is to create autonomy that propels the world forward.  Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving.  In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future.\n At Wayve, your contributions matter.  We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact.  \n Make Wayve the experience that defines your career!  \n The role \n As a Senior Machine Learning Engineer on Wayve's Measurement team in AI Evaluation, based in our Sunnyvale office, you will build the computer vision and scene understanding models Wayve uses to measure the performance of the Wayve Driver offline. You will adapt technology from our on-vehicle models and Wayve Foundation Models into offline models that understand coverage, mine rare events, and assess driving behaviour, and you will drive their accuracy and generalisation across vehicles, markets, and conditions. Measuring your own models rigorously is part of the work. You will define ground truth and correctness criteria across a complex driving taxonomy, and turn them into automated benchmarks and evidence that our validation pipelines and safety cases can stand on.\n The Measurement team builds and qualifies the scene understanding models Wayve uses to measure driving performance offline, after on-road runs and in simulation. Offline is where the interesting headroom is: more compute per frame, larger foundation models, and access to both past and future temporal context that the vehicle never has. The outputs are mission-critical, directly informing model development decisions and customer deliverables. You will work in a focused, high-impact senior team with strong ownership, access to fleet-scale camera, lidar, and simulation data, and close partners across on-vehicle modelling, evaluation, data curation, and simulation.\n Key responsibilities \n \n Develop the models - build, train, and fine-tune the scene understanding models at the centre of Wayve's offline measurement, adapting on-vehicle architectures and Wayve Foundation Models for offline use.\n Drive accuracy and generalisation - improve model performance across vehicle platforms, geographies, and driving conditions; diagnose failure modes and close the loop on blind spots.\n Exploit the offline environment - use the advantages the vehicle does not have: higher compute budgets, larger model capacity, bidirectional temporal context, and multi-task or joint representation learning.\n Measure what you build - benchmark your models, set quality bars, and use metrics and error analysis to steer the next iteration; treat measurement as the feedback that drives the modelling.\n Make the evidence credible - ensure benchmarked results are statistically defensible and fit to feed validation pipelines at scale and our broader safety cases, across the product portfolio.\n Align priorities and mentor - work day-to-day with on-vehicle modelling, evaluation, data curation, and simulation teams across sites; contribute to strong engineering and modelling practice; mentor others on the team; understand how the team's priorities connect to the wider division..\n \n About you  \n In order to set you up for success as a Senior Machine Learning Engineer at Wayve, we’re looking for the following skills and experience.  \n Essential  \n \n 4+ years in ML engineering, including training and shipping deep learning models in production, comfortable taking ambiguous modelling problems from scoping through to a working solution.\n Hands-on experience training modern computer vision models, including transformer-based and multimodal or VLM architectures for detection, segmentation, classification, or scene understanding, on camera and/or lidar sensor data.\n Experience adapting or fine-tuning large pretrained or foundation models, and training shared representations across multiple tasks or objectives (multi-stage or joint training), including real trade-offs across data and losses.\n Proficient in Python and ML frameworks (esp. PyTorch), with a solid foundation in software engineering practices and comfort with large-scale training.\n Strong ownership: research-literate and pragmatic, able to drive a significant modelling workstream with autonomy, collaborate across teams, and mentor less experienced engineers.\n Able to measure your own models: comfortable defining and","salary_min":311850,"salary_max":370000,"location":"Sunnyvale, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["deep-learning","computer-vision","fine-tuning","generative-ai","autonomous-vehicles","pytorch","robotics","distributed-systems"],"apply_url":"https://wayve.firststage.co/jobs?gh_jid=8728757002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-19T20:07:14Z","expires_at":"2026-09-29T13:43:27.827274Z","created_at":"2026-08-25T18:31:14.421095Z","updated_at":"2026-08-30T13:43:27.960326Z","company_name":"Wayve","company_slug":"wayve","company_logo_url":"https://www.google.com/s2/favicons?domain=wayve.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/0dfb8aab-f030-4de8-ae57-e7eae9cdd25c"},{"id":"a4364ce6-3d6d-47f4-b3bc-f55ccee23a51","company_id":"e3915539-5a8f-4461-9f26-06366a918674","title":"Senior Computer Vision Engineer","slug":"senior-computer-vision-engineer-0e56cdf5","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 Anduril’s Lattice software platform integrates together many sensors into a single cohesive view of the world, providing needed context for our users. Anduril’s Frontier AI team builds edge-compatible, generative AI systems into the Lattice software platform to provide features and products that improve autonomy and reduce cognitive burden on the warfighter. Specific applications include but are not limited to automating mission planning, battle-space understanding, voice-control of assets, and enabling higher-levels of autonomy. \n ABOUT THE JOB\n Computer Vision Engineers on Anduril's Frontier AI team build edge-compatible systems that transform Lattice's complex multi-sensor inputs into accurate, actionable understanding of the world. We integrate ambitious ML and CV algorithms directly into the Lattice platform to increase the fidelity, accuracy, and usefulness of battlefield awareness. These systems improve the experience of Anduril's existing customers, aid in the scaling of our business to new customers, use-cases, and business lines, and they create high-quality snapshots of the world that autonomous and agentic systems can operate on top of.\n WHAT YOU’LL DO \n \n Design, train, and deploy computer vision and perception models for edge-compatible, mission-critical environments\n Build multi-sensor perception systems that fuse imagery, video, and other sensor data into coherent views of the battlefield\n Improve the fidelity, accuracy, and robustness of Lattice's understanding of objects, activities, and environments across varied operational conditions\n Integrate state-of-the-art machine learning algorithms into production systems used by customers in real-world scenarios\n Develop mission-relevant evaluation frameworks, datasets, and benchmarks to measure perception performance in the field\n Partner closely with software, autonomy, product, and deployment teams to turn research ideas into operational capabilities\n Identify new perception and battlefield-understanding problems where advances in computer vision can unlock major product impact\n \n REQUIRED QUALIFICATIONS\n \n BS, MS, or PhD in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related technical field\n 5+ years of experience developing computer vision, perception, or machine learning systems in production or advanced research settings\n Strong experience with deep learning, object detection, and object tracking frameworks\n Strong programming skills in Python, with the ability to build reliable research and production workflows\n Strong desire to invent, implement, test, and deploy novel techniques that vastly improve upon state-of-the-art public methods to solve research problem specific to the defense space\n Experience training, evaluating, and iterating on vision models for detection, tracking, segmentation, classification, or sensor fusion tasks\n Experience deploying or optimizing ML systems for constrained, real-time, or edge compute environments\n Ability to work across the full lifecycle of applied ML, from problem formulation and data strategy through deployment and performance monitoring\n Eligible to obtain and maintain an active U.S. Secret security clearance\n \n PREFERRED QUALIFICATIONS\n \n Advanced degree with a focus on computer vision, perception, robotics, or machine learning\n Experience with multi-modal or multi-sensor fusion systems\n Experience deploying deep learning models to embedded, edge, or air-gapped environments\n Familiarity with real-time perception systems for autonomous platforms, defense applications, or other safety-critical systems\n Experience building large-scale datasets, labeling pipelines, or benchmarking infrastructure for vision systems\n Experience working in high-ownership startup environments or defense technology organizations\n Prior experience with geospatial, ISR, EO/IR, radar, or other battlefield-relevant sensing modalities\n US Salary Range\n $191,000 — $292,000 USD \n The salary range for this role is an estimate based on a wide range of compensation factors, inclusive of base salary only. Actual salary offer may vary based on (but not limited to) work experience, education and/or training, critical skills, and/or business con","salary_min":191000,"salary_max":292000,"location":"Washington, DC","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["payments","generative-ai","computer-vision","deep-learning","robotics","agents","cloud"],"apply_url":"https://boards.greenhouse.io/andurilindustries/jobs/5202963007?gh_jid=5202963007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-18T20:36:43Z","expires_at":"2026-09-29T13:37:21.495348Z","created_at":"2026-08-25T18:28:19.254045Z","updated_at":"2026-08-30T13:37:21.63641Z","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/a4364ce6-3d6d-47f4-b3bc-f55ccee23a51"},{"id":"7ed7422c-421c-4fee-b478-a18ac174105a","company_id":"e8c9f3a5-9310-43f5-9341-321fe6d93a92","title":"Principal Machine Learning Engineer, Geometric Vision","slug":"principal-machine-learning-engineer-geometric-vision-81629411","description":"About us    \n Founded in 2017, Wayve is the leading developer of Embodied AI technology.  Our advanced AI software and foundation models enable vehicles to perceive, understand, and navigate any complex environment, enhancing the usability and safety of automated driving systems.\n Our vision is to create autonomy that propels the world forward.  Our intelligent, mapless, and hardware-agnostic AI products are designed for automakers, accelerating the transition from assisted to automated driving.  In our fast-paced environment big problems ignite us—we embrace uncertainty, leaning into complex challenges to unlock groundbreaking solutions. We aim high and stay humble in our pursuit of excellence, constantly learning and evolving as we pave the way for a smarter, safer future.\n At Wayve, your contributions matter.  We value diversity, embrace new perspectives, and foster an inclusive work environment; we back each other to deliver impact.  \n Make Wayve the experience that defines your career!  \n The role  \n As a Principal Engineer on the Model Foundations team you will  build the geometric vision and 3D foundation models that underpin our autonomous driving systems.You will work at the intersection of large-scale deep learning, geometric computer vision, and real-world robotics, developing models that learn 3D structure and dynamics from fleet-scale sensor data.\n You will be a hands-on technical leader. You will set direction for geometric vision, prototype and train new model architectures, build the data and supervision needed to scale them, and take successful ideas through to deployment on real vehicles.\n Key responsibilities \n \n Design and train 3D foundation models and world models using large-scale driving data.\n Develop model architectures for 3D perception, geometric reasoning, reconstruction, and world modeling across space and time.\n Build scalable data generation and auto-labeling pipelines that produce high-quality geometric supervision from large volumes of sensor data.\n Develop and scale offline SLAM and 3D reconstruction systems and pipelines, using large-scale sensor data to recover accurate trajectories, scene geometry, calibration signals, and geometric supervision for model training and evaluation.\n Develop and apply techniques in multi-view geometry, neural rendering, NeRFs, Gaussian Splatting, implicit 3D representations, and feedforward 3D modeling.\n Explore geometry-aware tokenization and representation learning, including efficient ways to encode and fuse information across cameras, viewpoints, time, and sensing modalities.\n Develop foundation vision models that make effective use of camera, radar, LiDAR, and other sensor data for learning rich representations of the physical world.\n Explore video and generative modeling approaches for learning scene structure, dynamics, and future evolution from driving data.\n Train and evaluate models at scale on distributed compute, rapidly iterating on architectures, objectives, data, and training recipes.\n Develop automated evaluation and ground-truth systems for measuring geometric consistency, reconstruction quality, 3D understanding, and downstream driving performance.\n Optimize and deploy models into production autonomous-driving systems, working across model architecture, inference, and onboard constraints.\n Set technical direction for geometric vision at Wayve and work closely with researchers and engineers across foundation models, perception, simulation, data, sensing, and deployment.\n \n About you   \n In order to set you up for success as a Principal Machine Learning Engineer, Geometric Vision at Wayve, we’re looking for the following skills and experience.  \n Essential  \n \n Deep expertise in 3D computer vision, geometric vision, or 3D machine learning, with experience in areas such as multi-view geometry, neural rendering, reconstruction, implicit representations, or world modeling.\n Strong experience designing, training, and evaluating modern deep-learning models at scale, using PyTorch or a comparable framework.\n Strong mathematical and technical foundations in geometry, linear algebra, probability, optimization, and 3D transformations, combined with excellent software engineering skills in Python and C++\n A track record of taking difficult research problems from idea to working system, including building large-scale data, training, evaluation, or deployment pipelines.\n Principal-level technical leadership: the ability to identify high-leverage problems, set research and engineering direction, make strong architectural decisions, and raise the technical bar across teams.\n \n Desirable  \n \n 3D and geometric vision: multi-view geometry, dense 3D reconstruction, neural fields, NeRFs, Gaussian Splatting, or feedforward 3D models.\n Foundation and world models: large-scale vision pre-training, self-supervised learning, video models, generative models, or learned scene dynamics.\n Geometric data engines: offline SLAM, stru","salary_min":407330,"salary_max":460020,"location":"Sunnyvale, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["computer-vision","robotics","distributed-systems","autonomous-vehicles","pre-training","pytorch","generative-ai","deep-learning"],"apply_url":"https://wayve.firststage.co/jobs?gh_jid=8724862002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-18T19:30:55Z","expires_at":"2026-09-29T13:43:25.9237Z","created_at":"2026-08-25T18:31:14.335359Z","updated_at":"2026-08-30T13:43:26.052776Z","company_name":"Wayve","company_slug":"wayve","company_logo_url":"https://www.google.com/s2/favicons?domain=wayve.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/7ed7422c-421c-4fee-b478-a18ac174105a"},{"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":"d5aae1c9-cae9-47d5-9fce-478d44a8cb20","company_id":"4ed3e523-b627-46ed-8dae-04c2ea823be2","title":"Research Scientist/Research Engineer, Reinforcement Learning ","slug":"research-scientistresearch-engineer-reinforcement-learning-2476fcd9","description":"Jump Trading Group is committed to world-class research. We empower exceptional talents in Mathematics, Physics, and Computer Science to seek scientific boundaries, push through them, and apply cutting-edge research to global financial markets. Our culture is unique. Constant innovation requires fearlessness, creativity, intellectual honesty, and a relentless competitive streak. We believe in winning together and unlocking unique individual talent by incentivizing collaboration and mutual respect. At Jump, research outcomes drive more than superior risk-adjusted returns. We design, develop, and deploy technologies that change our world, fund start-ups across industries, and partner with leading global research organizations and universities to solve problems.\n Our team is a group of quantitative researchers, engineers, and ML experts leading reinforcement learning research and trading at Jump. Our mission is to combine emerging techniques and original research to learn optimal decision-making policies from financial market data and monetize them globally. We are building the future of ML-powered trading through breakthrough reinforcement learning, and we're looking for an exceptional Research Scientist/Research Engineer to join our team.\n What You’ll Do \n As a Research Scientist/Research Engineer working on RL, you'll be at the forefront of applying reinforcement learning to markets. You'll conduct original research and own the systems that turn it into production trading: designing and evaluating policy architectures, reward formulations, and objective horizons with rigorous out-of-sample benchmarking; partnering with trading and research teams to source, integrate, and validate their alpha signals within the RL framework; ensuring simulation fidelity against live trading by modeling market microstructure, fill dynamics, liquidity, and latency; building efficient tooling to store, process, and analyze very large volumes of market and signal data; and communicating findings to technical and trading audiences. This isn't incremental optimization; we're pushing the boundaries of what reinforcement learning can do at scale, where your improvements directly impact live trading.\n Other duties as assigned or needed. Skills You’ll Need \n \n 5+ years of experience developing reinforcement learning and/or deep learning systems with measurable impact in industry and/or academia\n Depth in reinforcement learning, including experience designing reward formulations, policy architectures, and evaluation, and taking RL methods from research into production\n Proficiency in Python and/or C++\n Familiarity with ML libraries/frameworks such as PyTorch (preferred), TensorFlow, and/or JAX\n Strong foundation in mathematics and statistics\n PhD or Master's degree in Computer Science, Machine Learning, Robotics (or a related subject)\n Strong publication record at ICML, ICLR, AAAI, NeurIPS, CVPR, or equivalent\n Ability to thrive in a collaborative, team-oriented environment\n Creative thinkers who are driven, self-motivated, and eager to solve challenging problems\n Reliable and predictable availability\n Excellent written and verbal communication skills in English\n Benefits \n \n Discretionary bonus eligibility \n Medical, dental, and vision insurance \n HSA, FSA, and Dependent Care options \n Employer Paid Group Term Life and AD\u0026D Insurance \n Voluntary Life \u0026 AD\u0026D insurance \n Paid vacation plus paid holidays \n Retirement plan with employer match \n Paid parental leave \n Wellness Programs \n \n Annual Base Salary Range \n $200,000 — $350,000 USD","salary_min":200000,"salary_max":350000,"location":"New York, NY","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["pytorch","tensorflow","search","deep-learning","reinforcement-learning","robotics","research"],"apply_url":"https://www.jumptrading.com/hr/job?gh_jid=8122860","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-18T18:54:10Z","expires_at":"2026-09-29T13:47:31.484855Z","created_at":"2026-08-27T13:48:12.175153Z","updated_at":"2026-08-30T13:47:31.614748Z","company_name":"Jump Trading","company_slug":"jump-trading","company_logo_url":"https://www.google.com/s2/favicons?domain=jumptrading.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/d5aae1c9-cae9-47d5-9fce-478d44a8cb20"},{"id":"b04c0a03-fc25-4cc1-85e1-9c6efc357643","company_id":"97187e1c-a220-4e7e-aa1e-cd5342f434c1","title":"Machine Learning Engineer","slug":"machine-learning-engineer-50e35dc4","description":"Who we are \n About Stripe \n Stripe, LLC. is a financial infrastructure platform for businesses. Millions of companies - from the world’s largest enterprises to the most ambitious startups - use Stripe to accept payments, grow their revenue, and accelerate new business opportunities. Our mission is to increase the GDP of the internet, and we have a staggering amount of work ahead. That means you have an unprecedented opportunity to put the global economy within everyone's reach while doing the most important work of your career.\n What you’ll do \n Responsibilities \n \n Design state-of-the-art ML models and large-scale ML systems for underwriting and portfolio management for Stripe Capital based on ML principles, domain knowledge, risk, regulatory and engineering constraints. \n Design systems to speed up the time from idea to deployment of new models. \n Experiment and iterate on ML models (using tools including PyTorch and TensorFlow) to achieve key business goals and drive efficiency. \n Develop pipelines and automated processes to train and evaluate models in offline and online environments. \n Integrate ML models into production systems and ensure their scalability and reliability. \n Collaborate with product and strategy partners to propose, prioritize, and implement new product features. \n Engage with the latest developments in ML/AI and take calculated risks in transforming innovative ML ideas into productionized solutions.\n \n Who you are \n Minimum requirements \n Must have a Bachelor's degree or foreign equivalent in Computer Science, Machine Learning, Mathematics, Physics, Statistics, or a related field, plus two (2) years of experience in Building and shipping ML systems in production.\n Must have two (2) years of experience in each of the following:\n \n ML algorithms and model architectures;\n Designing, training and evaluating machine learning models;\n Productionizing and deploying machine learning models at scale;\n Orchestrating data pipelines and leveraging large-scale datasets; and\n Building and deploying ML models to solve business problems.\n \n Must have one (1) year of experience in each of the following:\n \n ML libraries and frameworks including PyTorch, TensorFlow, XGBoost or Spark; and\n Deep learning, including transformers, test-time compute, or reinforcement learning.\n \n Salary: $212,000 - $318,000/yr.  \n This salary range represents the base salary range for the role and any sales commissions / sales bonuses targets, if applicable, would be in addition to the base salary.\n 40 hrs/week\n 50% Telecommuting Permitted.\n Multiple Positions Available. \n Additional benefits for this role may include: equity, company bonus or sales commissions/bonuses; 401(k) plan; medical, dental, and vision benefits; and wellness stipends. CA29 \n #LI-DNI","salary_min":212000,"salary_max":318000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["reinforcement-learning","tensorflow","pytorch","deep-learning","data-pipeline","payments","machine-learning"],"apply_url":"https://stripe.com/jobs/search?gh_jid=8137997","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-17T22:06:29Z","expires_at":"2026-09-29T13:34:35.944205Z","created_at":"2026-08-25T18:27:17.935027Z","updated_at":"2026-08-30T13:34:36.087082Z","company_name":"Stripe","company_slug":"stripe","company_logo_url":"https://www.google.com/s2/favicons?domain=stripe.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/b04c0a03-fc25-4cc1-85e1-9c6efc357643"},{"id":"a27af2e3-0c1e-45a0-8d92-2485073e28ff","company_id":"12105b3e-eb1d-4a92-95b6-855042facaf1","title":"Applied Scientist II","slug":"applied-scientist-ii-c3c7e3aa","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 Applied Scientist II \n  \n Why We Have This Role \n We are looking for talented and innovative Applied Scientist to bring our Core AI Machine Learning and Artificial Intelligence R\u0026D and strategy to the next level. Our goal is to personalize the Qualtrics experience using ML and AI features showcasing Qualtrics data as a core value proposition and competitive advantage.\n As an Applied Scientist at Qualtrics, you should love building cutting-edge predictive models to solve hard customer problems. Crafting models in an agile environment to withstand hyper growth and owning quality from end-to-end is a rewarding challenge and one of the reasons Qualtrics is such an exciting place to work!\n How You’ll Find Success \n \n Leverage your deep knowledge of artificial intelligence (AI) principles, including machine learning, natural language processing, computer vision, and reinforcement learning.\n Use your understanding of both supervised and unsupervised learning techniques, and their applications in building intelligent systems.\n Develop and optimize algorithms for building scalable and efficient GenAI applications.\n Tackle challenging problems in creative ways, leveraging generative models to address real-world use cases and drive innovation.\n Use effective communication skills to articulate technical concepts to non-technical stakeholders and gather requirements for GenAI application development.\n Show strong programming skills in languages like Python, along with proficiency in deep learning frameworks such as TensorFlow, PyTorch, or similar.\n \n How You’ll Grow \n \n Passion for leveraging cutting-edge AI technology to create innovative GenAI applications that have a meaningful impact on businesses, industries, and society.\n Commitment to developing GenAI applications that adhere to ethical standards and promote positive societal impact while minimizing potential risks.\n Drive to push the boundaries of what's possible with AI, and to contribute to the advancement of the field through research, experimentation, and collaboration.\n Willingness to stay updated with the latest advancements in AI research and technology, and to continuously learn and adapt to new methodologies and best practices.\n Agility to pivot and iterate on GenAI applications based on feedback, emerging trends, and changing business requirements.\n \n Things You’ll Do \n \n Address challenges in products through Large Language Models, Deep Learning and Data Science approaches and publish research papers.\n Work as part of a multidisciplinary team to research, implement, evaluate, optimize, productize and maintain cutting-edge machine learning models to meet the demands of our rapidly growing business\n Stay on top of the latest developments in machine learning and related research, and present research findings with the broader community\n Work closely with, and incorporate feedback from other specialists, engineers, and product managers\n Lead and engage in design reviews, modeling discussions, requirement definitions and other technical activities in diverse capacity\n Contribute to and inspire the Conversational AI, NLP, and Data Science technology roadmap at Qualtrics.\n Design, build, and evaluate Agentic AI systems to solve complex customer challenges.\n \n What We’re Looking For On Your Resume \n \n Bachelors and Ph.D in Computer Science or related fields\n Solid understanding of machine learning fundamentals and tool ecosystem\n 3+ years of combined academic and industrial research experience in machine learning, NLP, information retrieval, deep learning or a related field.\n Experience with Agentic AI systems, including design, development, and rigorous evaluation of agent performance.\n Deep learning implementation expertise (TensorFlow, PyTorch etc)\n Excellent command of at least one modern programming language (preferably Python)\n Deep understanding of machine learning model life cy","salary_min":155000,"salary_max":203500,"location":"Reston, VA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["llm","search","tensorflow","deep-learning","fine-tuning","agents","computer-vision","healthcare"],"apply_url":"https://www.qualtrics.com/careers/us/en/job/8115079?gh_jid=8115079","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-17T18:10:13Z","expires_at":"2026-09-29T13:49:13.405443Z","created_at":"2026-08-25T18:33:38.583782Z","updated_at":"2026-08-30T13:49:13.540253Z","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/a27af2e3-0c1e-45a0-8d92-2485073e28ff"},{"id":"d8eb6e88-6cb4-4d52-96e3-30a33b5d446e","company_id":"c93e0284-9c76-4a85-9905-494865ab9278","title":"Senior Principal Machine Learning Engineer","slug":"senior-principal-machine-learning-engineer-1289db26","description":"The era of pervasive AI has arrived. In this era, organizations will use generative AI to unlock hidden value in their data, accelerate processes, reduce costs, drive efficiency and innovation to fundamentally transform their businesses and operations at scale. \n SambaNova Suite™ is the first full-stack, generative AI platform, from chip to model, optimized for enterprise and government organizations. Powered by the intelligent SN40L chip, the SambaNova Suite is a fully integrated platform, delivered on-premises or in the cloud, combined with state-of-the-art open-source models that can be easily and securely fine-tuned using customer data for greater accuracy. Once adapted with customer data, customers retain model ownership in perpetuity, so they can turn generative AI into one of their most valuable assets. \n About the team\n The ML team builds and optimizes the models that run on SambaNova's RDU accelerators. Their work covers model architecture, training and fine-tuning, inference optimization, evaluation, and data curation, and it lands in SambaStack and SambaCloud. They work directly with the compiler, systems, and hardware teams on co-design, so decisions about a model shape decisions about the silicon it runs on.\n About the role \n As a Senior Principal Machine Learning Engineer, you will be responsible for designing, developing, and optimizing machine learning models—with a focus on cutting-edge Large Language Models (LLMs)—to run efficiently on SambaNova's specialized hardware architecture, including the RDU. This critical role bridges advanced LLM research and practical deployment, involving the development of model architectures, improving training and inference efficiency, and collaborating on hardware-software co-design with compiler, systems, and hardware teams. The engineer will also act as the ML expert, guiding the integration of LLM solutions into production systems and customer-facing products like SambaStack and SambaCloud, with work spanning the full lifecycle from training and inference to evaluation and data curation.\n Responsibilities \n Some of your responsibilities will include:\n \n Define and drive technical strategy for ML model development, training pipelines, and inference systems on SambaNova's RDU and broader hardware ecosystem\n Lead hardware-software co-design efforts in close collaboration with compiler, systems, and hardware teams—shaping architectural decisions that unlock performance at scale\n Identify, evaluate, and champion state-of-the-art ML techniques (e.g., speculative decoding, reinforcement learning, mixture-of-experts, long-context modeling) for adoption and adaptation on reconfigurable dataflow architectures\n Serve as the senior technical voice in critical design reviews, architectural decisions, and cross-functional planning—providing guidance that influences product and engineering roadmaps\n Mentor and develop principal and senior ML engineers, elevating the technical capabilities of the organization through active collaboration, design feedback, and knowledge transfer\n Partner with product and engineering leadership to translate complex ML capabilities into scalable, customer-facing solutions in SambaStack and SambaCloud\n Drive resolution of the most complex, ambiguous technical challenges —including those that span organizational boundaries or require novel approaches not yet established in the field\n \n Required Qualifications \n \n B.S. in Computer Science, Electrical Engineering, or related field\n 8+ years of industry experience in machine learning engineering, with a demonstrated record of technical leadership on large-scale or novel ML systems\n Deep expertise in LLM training, fine-tuning, inference optimization, and evaluation at scale\n Strong background in ML algorithms, deep learning architectures, and modern training methodologies, with the ability to critically evaluate and advance the state of the art\n Demonstrated ability to lead and align cross-functional technical efforts, mentor senior engineers, and influence organizational direction without direct management authority\n Track record of independently scoping and delivering high-complexity, high-ambiguity technical projects\n \n Preferred Qualifications \n \n M.S. or Ph.D. in Computer Science, Electrical Engineering, or related field\n Experience with hardware-software co-design with non-GPU accelerators\n Publications or open-source contributions in LLM training or inference\n Experience with speculative decoding, mixture-of-experts, or long-context modeling in production\n Experience with reinforcement learning for post-training\n Familiarity with compiler or kernel-level optimization for ML workloads\n Base Salary Range:\n Base Pay Range\n $220,000 — $300,000 USD \n Submission Guidelines Please note that in order to be considered an applicant for any position at SambaNova Systems, you must submit an application form for each position for which you believe you are qualified.  \n EEO Policy SambaN","salary_min":220000,"salary_max":300000,"location":"San Jose, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["deep-learning","llm","fine-tuning","generative-ai","reinforcement-learning","machine-learning"],"apply_url":"https://sambanova.ai/sambanova-available-positions/?gh_jid=6089843004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-16T15:41:38Z","expires_at":"2026-09-29T13:34:49.513266Z","created_at":"2026-08-25T18:27:21.867478Z","updated_at":"2026-08-30T13:34:49.649206Z","company_name":"SambaNova Systems","company_slug":"sambanova","company_logo_url":"https://www.google.com/s2/favicons?domain=sambanova.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/d8eb6e88-6cb4-4d52-96e3-30a33b5d446e"},{"id":"f5ed0945-789e-4523-9377-609285671969","company_id":"74257563-5513-4a8d-a0f7-01f00c59aed6","title":"Senior Machine Learning Engineer, Trust","slug":"senior-machine-learning-engineer-trust-af806995","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 Everyone at Airbnb thinks about trust, but our team obsesses over it daily. At the core of trust is safety, and thus we spend a significant amount of our time and energy keeping the community safe. The Trust team is responsible for developing the technology that helps protect our community and platform from fraud while also ensuring our hosts, guests, homes, and experiences meet our high standards. We constantly work to fight against online fraud (such as monetary loss, compromised accounts, spam and scam in messages, fake inventory, etc.) as well as offline fraud (theft, property damage, personal safety, etc.). We also work on onboarding and screening of users, and think about complex topics like identity and reputation to ensure that every interaction with Airbnb helps build trust in us and our community.\n The Trust Frontier AI team is where new AI technology for Trust gets invented and proven. We build specialized models for mission-critical trust and safety problems, develop the AI agents and agentic capabilities that automate trust decisions, and create the benchmarks and evaluation harnesses that keep decision quality high as those agents take on more autonomy. We work on problems before the answer is known — prototyping, experimenting, and iterating with our partner teams until a solution proves itself against real business and top line metrics.\n You'll work side-by-side with talented product managers, data scientists, software engineers, fraud intelligence, and operations teams. Together, you'll design and build ML solutions that have direct, meaningful impact on user trust, business success, and the global Airbnb community.\n The Difference You Will Make: \n As a Senior Machine Learning Engineer on the Trust Frontier AI team, you will actively contribute code and ideas that shape the next generation of AI systems protecting millions of Airbnb users. You'll own and deliver ML projects end-to-end, from framing an ambiguous problem and prototyping a solution, to training and productionizing models, to proving impact on top line metrics with front line teams.\n You'll work on abuse behavior detection that spans multiple defenses, on AI agents that make trust decisions autonomously, and on the evaluation and benchmarking work that makes those decisions trustworthy. Much of this work is early: you will help decide what to build, not only how to build it, and you'll see it through to measurable impact on the platform.\n A Typical Day:  \n \n Frame and prototype ML and agentic solutions for problems that do not yet have an established approach, in partnership with product managers, data scientists, and front line defense teams.\n Design, build, and productionize end-to-end Machine Learning pipelines — including feature engineering, model training, evaluation, and deployment — for both batch and real-time use cases.\n Build and improve abuse behavior detection that generalizes across defenses.\n Design, launch, and iterate on AI agents that automate trust decisions, including orchestration, tool interfaces, and the guardrails that hold quality steady as autonomy increases.\n Build benchmarks, evaluation harnesses, and instrumentation that let us measure agentic and model decision quality objectively, and use them to drive real improvements.\n Develop specialized models for trust and safety use cases, and use LLMs and AI agents to accelerate how we build models.\n Write, review, and ship clean, testable code — whether training a new model, improving an existing pipeline, or optimizing a feature for scalability and reliability.\n Work with large-scale structured and unstructured data to continuously improve ML models for Airbnb product, business, and operational use cases.\n Partner with front line defense teams to validate solutions through experiments and holdouts, and quantify their impact on business and operational metrics.\n Participate in code reviews, design discussions, and cross-team collaborations to contribute to a high-quality ML engineering culture.\n \n Your Expertise: \n \n 5-10 years of industry experience in applied Machine Learning, with a track record of building and productionizing models at scale.\n 1-2+ years of hands-on experience with LLMs and GenAI technologies, including building with agentic frameworks, orchestration, and evaluation.\n Strong programming skills in Python (required) and familiarity with Scala, Java, or equivalent.\n Solid understanding of Machine Learning best practices — e.g., training/serving skew minimization, A/B testing, feature engineering, model selection — and algorithms such as ","salary_min":200000,"salary_max":235000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["tensorflow","data-pipeline","agents","pytorch","generative-ai","llm","deep-learning","machine-learning"],"apply_url":"https://careers.airbnb.com/positions/8130355?gh_jid=8130355","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-14T21:27:57Z","expires_at":"2026-09-29T13:39:39.773826Z","created_at":"2026-08-25T18:29:15.515072Z","updated_at":"2026-08-30T13:39:39.906905Z","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/f5ed0945-789e-4523-9377-609285671969"},{"id":"b25eefc5-b8c1-4baf-9494-64c7a4eca526","company_id":"c93e0284-9c76-4a85-9905-494865ab9278","title":"Principal Compiler Engineer ","slug":"senior-principal-compiler-engineer-a021a7d0","description":"The era of pervasive AI has arrived. In this era, organizations will use generative AI to unlock hidden value in their data, accelerate processes, reduce costs, drive efficiency and innovation to fundamentally transform their businesses and operations at scale. \n SambaNova Suite™ is the first full-stack, generative AI platform, from chip to model, optimized for enterprise and government organizations. Powered by the intelligent SN40L chip, the SambaNova Suite is a fully integrated platform, delivered on-premises or in the cloud, combined with state-of-the-art open-source models that can be easily and securely fine-tuned using customer data for greater accuracy. Once adapted with customer data, customers retain model ownership in perpetuity, so they can turn generative AI into one of their most valuable assets. \n About the team \n The compiler team at SambaNova powers the RDU through a unique compilation strategy. By fusing kernels into dataflow graphs that run across the entire accelerator architecture, we avoid sequential kernel launches and eliminate the memory traffic that bottlenecks conventional hardware. This is a key differentiator and unlike any other product on the market. \n About the role \n As a Principal Compiler Engineer you'll lead critical areas of our compiler including the IR and pass infrastructure, model lowering and partitioning, tensor tiling, memory management, and mapping operations onto the fabric. You'll operate in a tightly coupled environment, influencing hardware design to optimize performance, and bring the technical judgement, cross functional diplomacy, foresight, and low-level programming depth that integrated system design demands. You'll work with senior team members to ensure career growth and engagement, mentor and develop early career talent, actively recruit for the organization, and participate in interviews.\n Responsibilities \n \n Lead compiler engineering through ensuring standard methodologies, enterprise product insertion and process evolution\n Work with peers, domain experts, developers, customers, and work across the enterprise seeking optimal solutions\n Develop, integrate, and implement products\n Provide support for proposals in key areas aligned with core team competencies\n \n Basic Qualifications \n \n Bachelor’s or Master’s Degree in Computer Science, Computer Engineering, or equivalent with 5-10 years of industry experience\n \n Additional Qualifications \n \n Deep theoretical understanding of compiler fundamentals\n Experience building and deploying software products\n Experience with one or more deep learning frameworks (i.e. TensorFlow, PyTorch) is a plus\n Experience with common compiler development practices and methodologies\n Excitement about high-performance systems engineering and performance debugging\n An appreciation for process and developing cross-disciplinary collaboration\n \n Preferred Qualifications \n \n Experience with MLIR\n Familiarity with machine learning models and frameworks\n Familiarity with accelerated computing\n Exposure to dataflow architectures\n Base Salary Range:\n Base Pay Range\n $180,000 — $255,000 USD \n Submission Guidelines Please note that in order to be considered an applicant for any position at SambaNova Systems, you must submit an application form for each position for which you believe you are qualified.  \n EEO Policy SambaNova Systems is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard basis of age (40 and over), color, disability, gender identity, genetic information, marital status, military or veteran status, national origin/ancestry, race, religion, creed, sex (including pregnancy, childbirth, breastfeeding), sexual orientation, and any other applicable status protected by federal, state, or local laws. \n Benefits Summary for US-Based, Full-Time Employment Positions SambaNova offers a competitive total rewards package, including the base salary, plus equity and benefits. We cover 95% premium coverage for employee medical insurance, and 77% premium coverage for dependents and offer a Health Savings Account (HSA) with employer contribution. We also offer Dental, Vision, Short/Long term Disability, Basic Life, Voluntary Life, and AD\u0026D insurance plans in addition to Flexible Spending Account (FSA) options like Health Care, Limited Purpose, and Dependent Care. Our library of well-being benefits available to you and your dependents includes a full subscription to Headspace, Gympass+ membership with access to physical gyms, One Medical membership, counseling services with an Employee Assistance Program, and much more.","salary_min":180000,"salary_max":255000,"location":"Austin, TX","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["tensorflow","deep-learning","pytorch","generative-ai"],"apply_url":"https://sambanova.ai/sambanova-available-positions/?gh_jid=6007939004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-14T13:58:02Z","expires_at":"2026-09-29T13:34:49.224959Z","created_at":"2026-08-25T18:27:21.859841Z","updated_at":"2026-08-30T13:34:49.359418Z","company_name":"SambaNova Systems","company_slug":"sambanova","company_logo_url":"https://www.google.com/s2/favicons?domain=sambanova.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/b25eefc5-b8c1-4baf-9494-64c7a4eca526"},{"id":"c78452e5-7fd6-46e4-931b-4317dab14624","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Perception Machine Learning Engineer - Continuous Learning","slug":"perception-machine-learning-engineer-continuous-learning-f4011dc8","description":"Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n As a Perception Machine Learning Engineer, you will build the intelligent systems that \"see\" the world, directly shaping the future of autonomous travel.\n Within the Perception team, we are tackling some of the most complex, open-ended challenges in autonomous driving. Our models must constantly adapt and improve as our fleet encounters the vast, unpredictable realities of public roads. We are looking for a Machine Learning Engineer to help design and build the automated, closed-loop systems that drive this continuous improvement.\n In this role, you will be the bridge between model architecture and large-scale data infrastructure. You will leverage active learning and sophisticated data curation strategies to ensure our perception models are always learning from the most informative examples. Crucially, this means managing the entire lifecycle of our data: intelligently selecting novel scenarios from the fleet while continuously pruning our existing corpus to maximize training efficiency.\n In this hybrid role you will report to a Technical Lead Manager.\n You will: \n \n Architect Infrastructure: Design and scale the data pipelines needed to mine, ingest, and manage massive volumes of sensor data from our fleet.\n Drive Model Improvement: Deploy active learning algorithms to continuously identify and select the most impactful data for training, ensuring our large models continuously adapt to new environments with incremental updates.\n Ensure Model Quality: Develop methods and recipes for evaluating real-world performance of our models, and detecting regressions in model updates.  Develop and maintain ground-truth free performance metrics.\n Optimize Data Efficiency: Conduct large-scale experiments focused on data balancing, subset selection, and label quality optimization. Lead automated curation strategies—including smart pruning and downsampling—to minimize dataset bloat and maximize compute efficiency.\n Solve Long-Tail Challenges: Develop robust mining, training and evaluation pipelines for rare, safety-critical real-world scenarios.\n Innovate with Model Signals: Utilize uncertainty estimation, confidence scores, and embedding space analysis to uncover model blind spots and guide automated data acquisition.\n Collaborate Cross-Functionally: Work closely with researchers and operations teams to iterate on the end-to-end model development lifecycle.\n \n You Have: \n \n A bachelor’s degree in Machine Learning, Robotics, or Computer Science. 3+ years of professional experience in Machine Learning and/or Computer Vision.\n Proven, hands-on experience applying active learning in production environments.\n Strong expertise in building large-scale ML data pipelines (mining, extraction, auto-labeling, ingestion).\n Deep understanding of data curation—balancing, core set selection, and sampling—to optimize model performance.\n Proficiency in Python and deep learning frameworks (PyTorch or JAX).\n Strong software engineering skills for writing robust, production-ready code.\n \n We Prefer: \n \n An advanced degree (MS or PhD) in Machine Learning, Robotics, or Computer Science.\n A record of publications at top-tier conferences (e.g., CVPR, ICCV, ECCV, ICML, ICLR, NeurIPS, IROS, RSS, AAAI, IJCV, PAMI).\n Experience with C++\n Experience building data-centric infrastructure from the ground up to accelerate model iteration cycles.\n The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.  \n Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.  \n Salary Range\n $175,000 — $215,000 USD","salary_min":175000,"salary_max":215000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["computer-vision","deep-learning","autonomous-vehicles","robotics","data-pipeline","pytorch","machine-learning"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=8127006","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-13T22:58:13Z","expires_at":"2026-09-29T13:35:05.310038Z","created_at":"2026-08-25T18:27:25.773269Z","updated_at":"2026-08-30T13:35:05.448509Z","company_name":"Waymo","company_slug":"waymo","company_logo_url":"https://www.google.com/s2/favicons?domain=waymo.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/c78452e5-7fd6-46e4-931b-4317dab14624"},{"id":"8cf9f724-5543-42e5-8ec7-6e485eeeb0a4","company_id":"72014eb6-e84d-48c2-af5c-5424ebec0b3c","title":"Senior Machine Learning Infrastructure Engineer, Embedding Platform","slug":"senior-machine-learning-infrastructure-engineer-embedding-platform-6b3a54da","description":"Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com .\n The LS Embedding Machine Learning Platform team is at the forefront of building highly expressive, machine learning models that power Reddit’s recommendation systems. We go beyond standard retrieval and ranking architectures, leveraging modern deep learning approaches and scalable model designs to enhance personalization across Reddit’s ecosystem. Our work impacts content discovery, user engagement, and platform growth at a massive scale.\n About the Role \n As a Senior Machine Learning Infrastructure Engineer , you will work across both model development and ML platform to build large-scale learning systems that improve recommendation and personalization on Reddit. At the senior level, you will own major technical components end to end: designing models, implementing training and evaluation pipelines, and driving production deployment in close partnership with ML platform, product, and cross-functional ML teams.\n Responsibilities \n \n Design, train, and improve large-scale machine learning platforms for recommendation or personalization systems.\n Own and deliver major ML systems components end to end, from problem framing through production rollout.\n Build and optimize end-to-end ML pipelines spanning data preparation, feature generation, training, evaluation, and deployment.\n Improve distributed training, model efficiency, and online inference performance.\n Apply modern modeling approaches including sequence modeling and related foundation-model techniques to Reddit use cases.\n Develop reliable serving and monitoring patterns for low-latency, high-throughput production ML systems.\n Work with cross-functional partners across product, relevance, ads, and core ML teams to deliver measurable improvements in user experience and business impact.\n Drive rigorous offline and online evaluation, including experimentation, model diagnostics, and feedback-loop improvement.\n Contribute to engineering quality through strong code, design reviews, documentation, and operational excellence.\n \n Qualifications \n \n 5+ years of experience in machine learning engineering, with a strong focus on large-scale ML infrastructure and recommendation or personalization systems.\n Expertise in modern deep learning architectures, including sequence models and foundational models.\n Experience building or scaling ML platform for large datasets and high-traffic production environments.\n Demonstrated ability to independently scope and execute ambiguous technical work, while owning high-quality implementation details.\n Solid understanding of distributed training and inference concepts, such as data parallelism, model parallelism, pipeline parallelism, or related optimization techniques.\n Proficiency in Python and experience with modern ML frameworks such as PyTorch, TensorFlow, or similar.\n Strong software engineering fundamentals, including system design, debugging, testing, and performance optimization.\n Experience with A/B testing, model evaluation frameworks, and real-time feedback loops in large-scale production systems.\n Excellent communication skills, with the ability to effectively present complex ML concepts to technical and non-technical stakeholders.\n \n Benefits: \n \n Comprehensive Healthcare Benefits and Income Replacement Programs\n 401k with Employer Match\n Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support\n Family Planning Support\n Gender-Affirming Care\n Mental Health \u0026 Coaching Benefits\n Flexible Vacation \u0026 Paid Volunteer Time Off\n Generous Paid Parental Leave \n \n #LI-Remote\n Pay Transparency: \n This job posting may span more than one career level.\n In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/ .\n To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. Final offer amounts are determined by multiple factors including, skills, depth of work experience and relevant licenses/credentials, ","salary_min":190800,"salary_max":267100,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["healthcare","pytorch","deep-learning","distributed-systems","tensorflow","infrastructure","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/reddit/jobs/8127022","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-12T23:02:49Z","expires_at":"2026-09-29T13:38:58.366394Z","created_at":"2026-08-25T18:28:56.636852Z","updated_at":"2026-08-30T13:38:58.502511Z","company_name":"Reddit","company_slug":"reddit","company_logo_url":"https://www.google.com/s2/favicons?domain=www.reddit.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/8cf9f724-5543-42e5-8ec7-6e485eeeb0a4"},{"id":"5034cc11-d680-45ce-88f8-98db61a33f70","company_id":"72014eb6-e84d-48c2-af5c-5424ebec0b3c","title":"Staff Machine Learning Infrastructure Engineer, Embedding Platform","slug":"staff-machine-learning-infrastructure-engineer-embedding-platform-4410257e","description":"Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 130 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com .\n The LS Embedding Machine Learning Platform team is at the forefront of building highly expressive machine learning models that power Reddit’s recommendation systems. We go beyond standard retrieval and ranking architectures, leveraging modern deep learning approaches and scalable model designs to enhance personalization across Reddit’s ecosystem. Our work impacts content discovery, user engagement, and platform growth at a massive scale.\n How You'll Have Impact \n As a Staff Machine Learning Infrastructure Engineer , you will own the technical direction for large-scale machine learning platform, guiding the development of advanced deep learning architectures and high-impact ML systems. You will partner with leadership to define ML roadmaps, drive innovation in scalable model design and training approaches, and ensure efficient, reliable deployment of ML models in production. This role offers an opportunity to influence key AI-driven systems across Reddit while mentoring and uplifting the team’s technical capabilities.\n What You’ll Do \n \n Architect and lead the development of next-generation, large-scale machine learning techniques.\n Define and execute the ML strategy, identifying opportunities to enhance personalization and recommendation quality across Reddit.\n Lead research initiatives on scalable machine learning systems and real-time model adaptation, bringing cutting-edge advancements into production.\n Partner with ML infrastructure teams to build high-performance, distributed training systems that efficiently scale across multiple GPUs and cloud environments.\n Establish and optimize real-time serving architectures for large-scale embeddings, ensuring low-latency inference and high throughput.\n Collaborate cross-functionally with teams in Feed Ranking, Ads, Content Understanding, and Core ML to integrate ML models into Reddit’s key AI-driven systems.\n Mentor and guide senior and mid-level ML engineers, fostering a culture of excellence, innovation, and knowledge sharing.\n Stay at the forefront of AI research, evaluating and introducing new modeling paradigms to keep Reddit’s ML ecosystem cutting-edge.\n Drive technical discussions, present findings to leadership, and contribute to long-term ML planning and decision-making.\n \n Who You Might Be: \n \n 8+ years of experience in machine learning engineering, with a strong focus on large-scale ML systems and recommendation or personalization systems.\n Expertise in modern deep learning architectures, including sequence models and foundational models.\n Deep understanding of complex multi-entity relationships in machine learning applications and how they are modeled in large-scale systems.\n Proven ability to design, implement, and optimize scalable ML architectures, from distributed training to real-time inference.\n Strong software engineering skills in Python, C++, or similar languages, with experience in ML infrastructure, high-performance computing, and cloud-based ML pipelines.\n Demonstrated leadership in driving ML strategy, mentoring engineers, and influencing cross-functional teams.\n Experience with A/B testing, model evaluation frameworks, and real-time feedback loops in large-scale production systems.\n Excellent communication skills, with the ability to effectively present complex ML concepts to technical and non-technical stakeholders. \n \n Benefits: \n \n Comprehensive Healthcare Benefits and Income Replacement Programs\n 401k with Employer Match\n Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support\n Family Planning Support\n Gender-Affirming Care\n Mental Health \u0026 Coaching Benefits\n Flexible Vacation \u0026 Paid Volunteer Time Off\n Generous Paid Parental Leave \n \n #LI-Remote\n Pay Transparency: \n This job posting may span more than one career level.\n In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit https://www.redditinc.com/careers/ .\n To provide greater transparency to candidates, we share base salary ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchma","salary_min":253300,"salary_max":354600,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"lead","tags":["deep-learning","distributed-systems","healthcare","infrastructure","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/reddit/jobs/8126982","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-12T22:33:37Z","expires_at":"2026-09-29T13:39:01.21974Z","created_at":"2026-08-25T18:28:56.767146Z","updated_at":"2026-08-30T13:39:01.355053Z","company_name":"Reddit","company_slug":"reddit","company_logo_url":"https://www.google.com/s2/favicons?domain=www.reddit.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/5034cc11-d680-45ce-88f8-98db61a33f70"},{"id":"7b9b8809-f3ac-4c1b-9808-82ec279c5fb4","company_id":"a0000000-0000-0000-0000-000000000001","title":"Software Engineer, Infrastructure, Interpretability","slug":"software-engineer-infrastructure-interpretability-3cb5ab0f","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 When you see what modern language models are capable of, do you wonder, \"How do these things work? How can we trust them?\"\n The Interpretability team at Anthropic works to understand what's actually happening inside trained models - and applies our best techniques to keep frontier AI safe as it rapidly improves.\n Think of us as doing \"neuroscience\" of neural networks using \"microscopes\" we build - or reverse-engineering neural networks like binary programs.\n More resources to learn about our work: \n \n \n Our Research blog - covering advances including Monosemantic Features and Circuits \n \n An Intro to Interpretability from our research lead, Chris Olah \n \n The Urgency of Interpretability from CEO Dario Amodei\n \n Engineering Challenges Scaling Interpretability - directly relevant to this role\n \n 60 Minutes segment - see a demo of tooling our team built\n \n New Yorker article - what it's like to work on one of AI's hardest open problems\n \n This role is an early hire on a new infrastructure effort within Interpretability: you'll help define its charter, not just execute it. \n Interpretability research requires deep access to frontier models while retaining a high degree of research flexibility. Your job is to build the paved path that makes that access secure by default, private by design, and low-friction for every researcher. The work spans four areas:\n \n \n Security : design the secure-by-default environments and access patterns that enable deep model access for an organization whose research requires it - done well, the same design improves both our security posture and research productivity.\n \n Privacy : build data-access patterns that ensure policy adherence as our research moves from theory into practical application\n \n Data \u0026 Compute Management : manage research data at petabyte scale and make efficient use of large accelerator fleets - storage lifecycle, capacity planning, and scheduling.\n \n Developer experience : agentic engineering, tooling and observability that keep researchers moving fast\n \n In this role, you’ll be deeply embedded alongside Interp Researchers to understand their workflows - building your understanding of the research as you go; at the same time you’ll bridge communication with Anthropic’s wider platform and security teams.. Every hour of researcher friction you remove is multiplied across the whole organization, and the infrastructure you build sets the pace at which interpretability results reach real safety decisions.\n Responsibilities:\n \n \n Design, build, and own shared infrastructure for Interpretability - research environments, data systems, and compute tooling that researchers rely on daily\n \n Lead cross-team efforts with our agentic engineering , security, compute, and storage platform teams, so that company-wide solutions serve research needs\n \n Discover and resolve major organization-wide developer experience issues\n \n Help take interpretability methods from research code to dependable audit pipelines\n \n You may be a good fit if you:\n \n \n Are highly proficient in at least one programming language (e.g., Python, Rust, Go, Java) and productive with Python\n \n Have significant experience building and operating secure and scalable software infrastructure - cloud systems, distributed systems, or developer tooling\n \n Have strong cross-functional communication skills - equally at home working with researchers and with platform and security teams\n \n Are extremely curious about unfamiliar domains\n \n Have a strong ability to prioritize the most impactful work and are comfortable operating with ambiguity and questioning assumptions\n \n Are curious about interpretability research and its role in AI safety (though no research experience is required!)\n \n Care about the societal impacts and ethics of your work\n \n Strong candidates may also have:\n \n \n Experience with cloud infrastructure (e.g. GCP or AWS), Kubernetes, networking and infrastructure-as-code\n \n Security engineering experience: identity / auth / access management, sandboxing, red teaming\n \n Experience with data warehousing, large-scale storage systems, and data lifecycle management - especially for research\n \n Experience with compute schedulers and accelerator fleet management\n \n Experience building developer productivity tooling and observability stacks\n \n Experience building tooling to accelerate research teams\n \n Representative Projects:\n \n \n Design and stand up a hardened research environment where researchers experiment directly on frontier model weights\n \n Build lifecycle management for petabytes of research data - visibility, retention, and cost efficiency\n \n B","salary_min":320000,"salary_max":485000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["cloud","security","deep-learning","agents","distributed-systems","alignment","infrastructure","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5388612008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-12T22:29:15Z","expires_at":"2026-09-29T13:30:35.12076Z","created_at":"2026-08-25T18:26:19.069491Z","updated_at":"2026-08-30T13:30:35.264073Z","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/7b9b8809-f3ac-4c1b-9808-82ec279c5fb4"},{"id":"9edd9869-f57d-4db5-99ed-652d09a55cdf","company_id":"83c597c2-a4b2-4517-99df-1ac8c90756d5","title":"Machine Learning Engineer, II - 3D Perception","slug":"machine-learning-engineer-ii-3d-perception-7d1b0129","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.\n 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. \n 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 Torc's Multi-Modal Perception team is responsible for developing the machine learning systems that enable our autonomous trucks to perceive and understand the world around them. By combining information from cameras, LiDAR, and other sensor modalities, the team builds production-ready perception capabilities that provide the foundation for safe, reliable autonomous driving.\n As a Machine Learning Engineer II – 3D Perception, you'll join a collaborative team of machine learning engineers and researchers focused on solving complex real-world perception challenges. This role is primarily focused on advancing our Bird's Eye View (BEV) perception capabilities by developing, evaluating, and improving production machine learning solutions that support environmental understanding, model robustness, and system performance across Torc's autonomy stack.\n What You'll Do \n \n Design, develop, and improve machine learning models supporting Torc's perception systems.\n Own model development and delivery for well-defined perception problem areas, from data preparation and training through evaluation and integration.\n Write production-quality Python and PyTorch code to support scalable training, evaluation, and inference workflows.\n Analyze model performance, identify failure modes, and independently troubleshoot issues to improve robustness, accuracy, and generalization.\n Develop and evaluate perception models leveraging multi-modal sensor data, with an emphasis on camera-based and 3D perception systems.\n Collaborate with software engineers, infrastructure teams, and autonomy engineers to integrate perception models into larger production software systems.\n Contribute to improvements in training pipelines, data workflows, experimentation tooling, and developer workflows that accelerate model iteration and deployment.\n Participate in model architecture discussions and contribute technical recommendations within the team.\n Lead small technical initiatives or model components with guidance from senior engineers.\n Support and mentor Machine Learning Engineer I team members on implementation, experimentation, and machine learning best practices.\n Document technical work, evaluation results, and design decisions to support knowledge sharing and long-term maintainability.\n \n What You'll Need to Succeed \n \n Bachelor's degree in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related technical field with  4 + years of relevant industry experience, OR Master's degree with  3 + years of relevant experience, or equivalent practical experience.\n Experience developing machine learning models for computer vision, perception, robotics, autonomous systems, or a closely related domain.\n Strong programming skills in Python and PyTorch, with experience writing maintainable, production-quality machine learning code.\n Experience training, evaluating, and improving deep learning models using large-scale datasets.\n Experience working with image-based and/or 3D perception systems.\n Solid understanding of deep learning architectures commonly used for perception applications.\n Experience debugging model behavior, analyzing performance metrics, and proposing practical improvements.\n Ability to independently execute complex machine learning work within well-defined problem areas.\n Experience collaborating cross-functionally to integrate machine learning models into larger software systems.\n Strong problem-solving skills with the ability to operate effectively in an environment with evolving technical challenges and requirements.\n \n Bonus Points \n \n Experience developing perception systems for autonomous driving, robotics, or ADAS.\n Experience with LiDAR, point cloud processing, sensor fusion, BEV representations, or other 3D perception techniques.\n Experience with temporal perception models or video-based learning.\n Experience with C++, ROS, or robotics software development.\n Experience deploying machine learning models into production autonomy or robotics platforms.\n Experience working with large-scale perception datasets and distributed training environments.\n Familiarity with perception evaluation frameworks, model validation, and performance benchmarking.\n Experience improving ML tooling, automation, training workflows, or experimentation infrastructure.\n Experience leading a small technical ","salary_min":153200,"salary_max":183800,"location":"Ann Arbor, MI","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["distributed-systems","autonomous-vehicles","pytorch","computer-vision","robotics","deep-learning","payments","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/torcrobotics/jobs/8695202002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-11T18:34:22Z","expires_at":"2026-09-29T13:36:06.79112Z","created_at":"2026-08-25T18:27:50.457684Z","updated_at":"2026-08-30T13:36:06.925933Z","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/9edd9869-f57d-4db5-99ed-652d09a55cdf"}],"page":1,"per_page":20,"total":680,"total_is_exact":true,"total_pages":34}