{"access":{"catalog_url":"https://aidevboard.com/api/v1/catalog","description":"Public read endpoints are open and free. 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Consumer appetites for on-demand services are increasing, but current delivery methods are inefficient, costly, and contribute to road accidents and air pollution. Wing’s fleet of highly automated delivery drones can transport small packages directly from businesses to homes on-demand, in minutes. We design, build, and operate our aircraft, and offer drone delivery services on two continents. Our technology is designed to be easy to integrate into existing delivery and logistics networks, offering a scalable drone delivery solution for a broad range of businesses. Wing is a part of Google's parent company, Alphabet, and our mission is to create the preferred means of delivery for the planet. If you're ready to do the greatest work of your life, come join us. \n About the Program:  \n Join Wing's 12-week paid summer internship! This is a hybrid, in-person program based in Palo Alto, CA, with start dates in May or June. Eligible candidates must be a current student enrolled in a degree program and intend to return to the program after the completion of the summer internship. \n About the Opportunity: \n Wing is looking for a Machine Learning Engineer Intern to join our Perception team based in Palo Alto, CA. You will be at the forefront of advancing our autonomous drone delivery system, leveraging deep learning to ensure our aircraft navigate the world with precision and safety.  At Wing, you aren’t just building models in a vacuum; you have access to tens of thousands of hours of flight data from a diverse sensor suite\n In this role, you will: \n \n Help us track towards our goal of building unified and/or generalized Perception models that helps Wing aircraft systems gracefully handle a wide variety of edge cases they experience when operating in the real world\n Analyze real-world data, coming up with new ideas on how to improve our existing Perception pipelines with state-of-the-art foundational and end-to-end geometric models\n Gain experience with leveraging large amounts of diverse, real-world data from a fleet of thousands of robots to build ML models that solve real problems.\n Make a measurable impact on Wing's ability to scale our operations more rapidly, at the forefront of the rapidly growing drone delivery industry.\n Be a part of a dynamic and collaborative team.\n \n What You’ll Need:  \n \n Currently pursuing a Master's, or PhD degree in Aerospace Engineering, Mechanical Engineering, Computer Science, Computer Vision, Robotics, Electrical Engineering, or related field, graduating between December 2027 and August 2028\n Strong examples of previous engineering experience from past internships and/or academic or extracurricular projects.\n Strong grasp of basic physics and engineering fundamentals in Computer vision\n Strong grasp of modern computer vision technologies such as self-supervised Vision Transformer foundation model, Vision Language Models, VLAs, and hands-on experience in at-least one of those domains\n Experience with C++ and/or Python.\n The US base salary range for this internship position is the salary range below + benefits. Wing’s salary ranges are determined by role, level, and location. Your recruiter can share more about the specific salary range for your location during the hiring process.\n Salary Range\n $128,000 — $135,000 USD \n Wing is an equal opportunity employer and it is Wing's policy to comply with all applicable national, state and local laws pertaining to nondiscrimination and equal opportunity. Employment at Wing is based solely on a person's merit and qualifications directly related to professional competence. Wing does not discriminate against any employee or applicant because of race, creed, color, religion, gender, sexual orientation, gender identity/expression, national origin, disability, age, genetic information, veteran status, marital status, pregnancy or related condition (including breastfeeding), or any other basis protected by law.\n If you have a need that requires accommodation during the interview process due to a disability or special need, please let us know by completing our  Candidate Accommodations Request Form .","salary_min":128000,"salary_max":135000,"location":"Palo Alto, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["robotics","computer-vision","cloud","deep-learning","generative-ai","machine-learning"],"apply_url":"https://wing.com/careers/8872479002?gh_jid=8872479002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T22:00:01Z","expires_at":"2026-11-13T17:02:54.727732Z","created_at":"2026-10-14T15:24:57.757978Z","updated_at":"2026-10-14T17:02:54.730712Z","company_name":"Wing","company_slug":"wing","company_logo_url":"https://www.google.com/s2/favicons?domain=wing.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/8ad2920a-4171-4759-864b-2007360008f4"},{"id":"7f073f29-8892-4033-ab21-b488ea264615","company_id":"d90e5b83-7941-4bd8-aace-5327c84c09c6","title":"Software Engineer, Applied Machine Learning","slug":"software-engineer-applied-machine-learning-b6a1a5f9","description":"fal is the generative media ecosystem powering the next generation of AI products. We build the infrastructure, tools, and model access that teams need to move from idea to production, and do it at scale without compromise. For developers and enterprises, fal is the foundation that makes generative media not just possible, but practical: a unified platform where high-performance inference, orchestration, and observability come together to unlock new categories of AI-native products.\n\nAs generative media reshapes industries across a market projected to grow by hundreds of billions over the next decade, fal is becoming the ecosystem that ambitious teams build on.\n\n\n\n\nABOUT THIS ROLE:\n\nWe are seeking a hands-on, production-focused Applied Machine Learning Engineer to take technical ownership of the model layer powering our next-generation generative media platform. In this role, you will bridge the gap between cutting-edge generative research and scalable, consumer-facing products.\n\nYou will split your time between extending SOTA open-source models with additional capabilities and helping maintain our fleet of generative model APIs.\nWhat you’ll do:\n\n - Novel Model Pipelines: Work with our post-training team to extend SOTA image, video, audio, and 3D models with additional capabilities and modalities. Develop novel approaches to model conditioning, generation, and editing, including both training-free methods and model fine-tuning.\n\n - Fine-Tuning: Leverage our massive GPU fleet to fine-tune generative models for novel capabilities. Build and maintain fine-tuning APIs that allow customers to customize models for their specific needs.\n\n - Architecture \u0026 Abstraction: Identify common patterns across the models we serve and develop reusable components, abstractions, and building blocks that accelerate the development of new model capabilities and inference pipelines.\n\n - Inference Optimization: Work hand-in-hand with our ML Performance \u0026 Optimization team to apply state-of-the-art inference techniques and best practices, ensuring models run efficiently with low latency, high throughput, and optimal GPU utilization.\n\n - Production Deployment: Build, deploy, and maintain scalable, reliable generative model APIs. Anticipate and resolve production challenges to ensure our models serve customers reliably at scale.\n\n - Customer Collaboration: Work directly with customers, including some of the world's largest e-commerce retailers and film and TV production studios, to develop novel solutions to their generative media needs.\n\nQualifications/Nice-to-haves:\n\n - Experience: 3+ years of professional experience as an Applied ML Engineer, with at least 1–2 years focused on generative media or computer vision.\n\n - Core Frameworks: Expert-level proficiency in Python and PyTorch.\n\n - Generative Media: Deep practical understanding of diffusion and flow-based generative models.\n\n - Open-Source Tooling: Hands-on experience working with open-weight model ecosystems, including Hugging Face, Diffusers, and related tooling.\n\n - Engineering Rigor: Ability to anticipate and solve challenges that arise when deploying ML models to production. Strong engineering judgment in designing systems that are scalable, reliable, secure, safe, and performant.\n\n - Training-Free Model Extensions: Experience designing and implementing training-free extensions to image, video, audio, or 3D generative models, such as novel conditioning methods, inference-time modifications, or new model capabilities.\n\n - Model Post-Training: Experience developing and executing custom post-training or fine-tuning approaches to extend generative models with additional capabilities.\n\n - Startup Experience: Track record of working in fast-paced startup environments or digital media and entertainment industries.\n\n - Ability to Ship: Demonstrated ability to independently take ambitious ML ideas from concept to production.\n\nWhat we offer at fal:\n\n - Interesting and challenging work\n\n - A lot of learning and growth opportunities\n\n - Health, dental, and vision insurance (US)\n\n - Regular team events and offsites\n\n\nCOMPENSATION\n\n - $180,000 - $230,000 + equity + comprehensive benefits package\n\n \n\n\n\nU.S. EQUAL EMPLOYMENT OPPORTUNITY INFORMATION:\n\nfal provides equal employment opportunities to applicants and employees without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, protected veteran status, disability, or any other classification protected by applicable law.","salary_min":180000,"salary_max":230000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["computer-vision","pytorch","fine-tuning","machine-learning"],"apply_url":"https://jobs.ashbyhq.com/fal-ai/c351a0de-1826-4968-a023-a5243aba2ec6/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T21:14:59.835Z","expires_at":"2026-11-13T16:57:32.416891Z","created_at":"2026-10-14T15:36:11.028291Z","updated_at":"2026-10-14T16:57:32.420794Z","company_name":"Fal","company_slug":"fal","company_logo_url":"https://www.google.com/s2/favicons?domain=fal.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/7f073f29-8892-4033-ab21-b488ea264615"},{"id":"b34f4013-d975-493b-8a19-92a0a531c511","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Staff Software Engineer, Machine Learning","slug":"staff-software-engineer-machine-learning-c84454fb","description":"About the Team \n DoorDash is building the world's most reliable on-demand, logistics engine for delivery. We are continuing to grow rapidly and expanding our Engineering offices globally! We are looking for Software Engineers, Machine Learning to build and maintain a large scale 24x7 global infrastructure system that powers DoorDash's 3-sided marketplace of Consumers, Merchants and Dashers.\n About the Role \n As a Software Engineer, Machine Learning, you’ll be conceptualizing, designing, implementing, and validating algorithmic improvements to the catalog system and our product knowledge graph at the heart of our fast-growing grocery and retail delivery business.  You will use our robust data and machine learning infrastructure to implement new ML solutions to make our product knowledge graph accurate, standardized, semantically rich, easily discoverable, and extensible. We’re looking for someone with a command of production-level machine learning and experience with solving end-user problems who enjoys collaborating with multi-disciplinary teams.\n This role is hybrid with some in-office time expected and will report to an Engineering Manager. \n You're excited about this opportunity because you will... \n \n Develop advanced machine learning models to improve ads efficiency and quality.\n Design and build optimization algorithms for budget pacing and automated bidding to achieve various advertising goals. \n Establish a data-driven framework to understand how the bid density and market competitiveness would affect advertising value and platform revenue. \n Develop new data solutions (eg. embeddings and consumer profiles) to target the relevant audience.\n Be responsible for the end-to-end ML lifecycle, including ideation, offline model training, online shadowing/deployment, experimentation, and post-launch monitoring/measurement.\n Build and extend the current data/ML infrastructure to empower Ads data applications including data analysis, ML modeling, and experimentation.\n Scale our systems and services to fuel the growth of our business.\n \n We're excited about you because you have... \n \n M.S., or PhD. in a technical field such as computer science, mathematics, statistics, physics or equivalent.\n 8+ years ML industry experience with a solid understanding of machine learning algorithms and fundamentals.\n Proficiency in using AI coding tools (e.g., Claude Code, Codex, Cursor) in the full software development lifecycle, including designing, generating code, testing, monitoring and releasing software\n Experience building data/feature engineering pipelines at scale using Pyspark and Snowflake SQL.\n Experience with building machine learning systems in production by using frameworks such as PyTorch, Keras, LightGBM, scikit-learn, Spark ML, or related.\n Compensation \n The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others.\n To learn more about our benefits, visit our careers page here .\n See below for paid time off details:\n \n For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year.\n For hourly roles: vacation accrued at about 1 hour for every 25.97 hours worked (e.g. about 6.7 hours/month if working 40 hours/week; about 3.4 hours/month if working 20 hours/week), and paid sick time accrued at 1 hour for every 30 hours worked (e.g. about 5.8 hours/month if working 40 hours/week; about 2.9 hours/month if working 20 hours/week).\n \n The national base pay range for this position within the United States, including Illinois and Colorado.\n $203,500 — $299,300 USD \n About DoorDash\n At DoorDash, our mission to empower local economies shapes how our team members move quickly, learn, and reiterate in order to make impactful decisions that display empathy for our range of users—from Dashers to merchant partners to consumers. We are a technology and logistics company that started by enabling door-to-door delivery, and we are looking for team members who can help us go from","salary_min":203500,"salary_max":299300,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["pytorch","cloud","healthcare","fine-tuning","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8092262","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T17:49:36Z","expires_at":"2026-11-13T17:03:28.820143Z","created_at":"2026-10-14T15:25:31.461436Z","updated_at":"2026-10-14T17:03:28.823202Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/b34f4013-d975-493b-8a19-92a0a531c511"},{"id":"78fae61b-8780-438a-b75a-b7c5778b3828","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Senior Software Engineer, Machine Learning","slug":"senior-software-engineer-machine-learning-851e39b8","description":"About the Role \n As a Senior Software Engineer, Machine Learning, you’ll be conceptualizing, designing, implementing, and validating algorithmic improvements to the catalog system and our product knowledge graph at the heart of our fast-growing grocery and retail delivery business.  You will use our robust data and machine learning infrastructure to implement new ML solutions to make our product knowledge graph accurate, standardized, semantically rich, easily discoverable, and extensible. We’re looking for someone with a command of production-level machine learning and experience with solving end-user problems who enjoys collaborating with multi-disciplinary teams. We expect Senior Engineers to be able to own projects end-to-end and mentor their junior and mid level peers.\n This role is hybrid with some in-office time expected and will report to an Engineering Manager. \n You're excited about this opportunity because you will... \n \n Develop advanced machine learning models to improve core parts of your teams scope.\n Be responsible for the end-to-end ML lifecycle, including ideation, offline model training, online shadowing/deployment, experimentation, and post-launch monitoring/measurement.\n Work on creating novel ML \u0026 AI solutions that push both our business and the industry forward.\n Scale our systems and services to fuel the growth of our business.\n Completely disrupt logistics by tackling bleeding-edge, technical problems\n \n We're excited about you because you have... \n \n M.S., or PhD. in a technical field such as computer science, mathematics, statistics, physics or equivalent.\n 5+ years ML industry experience with a solid understanding of machine learning algorithms and fundamentals.\n Proficiency in using AI coding tools (e.g., Claude Code, Codex, Cursor) in the full software development lifecycle, including designing, generating code, testing, monitoring and releasing software\n Experience building data/feature engineering pipelines at scale using Pyspark and Snowflake SQL.\n Experience with building machine learning systems in production by using frameworks such as PyTorch, Keras, lightgbm, scikit-learn, Spark ML, or related.\n Compensation \n The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others.\n To learn more about our benefits, visit our careers page here .\n See below for paid time off details:\n \n For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year.\n For hourly roles: vacation accrued at about 1 hour for every 25.97 hours worked (e.g. about 6.7 hours/month if working 40 hours/week; about 3.4 hours/month if working 20 hours/week), and paid sick time accrued at 1 hour for every 30 hours worked (e.g. about 5.8 hours/month if working 40 hours/week; about 2.9 hours/month if working 20 hours/week).\n \n The national base pay range for this position within the United States, including Illinois and Colorado.\n $167,800 — $246,800 USD \n About DoorDash\n At DoorDash, our mission to empower local economies shapes how our team members move quickly, learn, and reiterate in order to make impactful decisions that display empathy for our range of users—from Dashers to merchant partners to consumers. We are a technology and logistics company that started by enabling door-to-door delivery, and we are looking for team members who can help us go from a company that is known as the place you order food to a company that people turn to for any and all goods. DoorDash is growing rapidly and changing constantly, which gives our team members the opportunity to share their unique perspectives, solve new challenges, and own their careers. We're committed to supporting employees’ happiness, healthiness, and overall well-being by providing comprehensive benefits and perks including premium healthcare, wellness expense reimbursement, paid parental leave and more.\n Our Commitment to Diversity and Inclusion\n We’re committed to growing and empowering a more inclusive","salary_min":167800,"salary_max":246800,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["cloud","healthcare","pytorch","fine-tuning","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8143205","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T17:38:25Z","expires_at":"2026-11-13T17:03:28.716694Z","created_at":"2026-10-14T15:25:31.359543Z","updated_at":"2026-10-14T17:03:28.719518Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/78fae61b-8780-438a-b75a-b7c5778b3828"},{"id":"634e56a9-b38d-474b-847e-3dc417b38339","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Staff Machine Learning Engineer, Personalization","slug":"staff-machine-learning-engineer-personalization-d2d9e9a7","description":"About the Team \n Come help us build the world's most reliable on-demand, logistics engine for last-mile retail delivery! We're looking for an experienced machine learning engineer to help us develop modern growth and personalization models that power DoorDash's growing retail and grocery business.\n About the Role \n We’re looking for a passionate Applied Machine Learning expert to join our team. As a Staff Machine Learning Engineer, you’ll be conceptualizing, designing, implementing, and validating algorithmic improvements to the growth and personalization experiences at the heart of our fast-growing grocery and retail delivery business.  You will use our robust data and machine learning infrastructure to implement new ML solutions to make the consumer search experience more relevant, seamless, and delightful across grocery, convenience, and many other retail categories. You will demonstrate a strong command of production level machine learning,  experience with solving end-user problems, and collaborate well with multi-disciplinary teams.\n You will report into the engineering manager on our Personalization team. We expect this role to be hybrid with some time in-office and some time remote (#LI-Hybrid).\n You’re excited about this opportunity because you will… \n \n Develop production machine learning solutions to build a world class personalized shopping experience for a diverse and expanding retail space\n Partner with engineering and product leaders to help shape the product roadmap applying ML\n Mentor junior team members, and lead cross functional pods to create collective impact\n \n You can find out more on our ML blog here \n We’re excited about you because you have… \n \n 8+ years of industry experience developing machine learning models with business impact, and shipping ML solutions to production. \n Proficiency in using AI coding tools (e.g., Claude Code, Codex, Cursor) in the full software development lifecycle, including designing, generating code, testing, monitoring and releasing software\n M.S., or PhD. in Statistics, Computer Science, Math, Operations Research, Physics, Economics, or other quantitative field\n Expertise in applied ML for  Causal Inference and Recommendation Systems  -  both classical and deep learning based. Additional familiarity with explore/exploit/MAB algorithms \u0026 LLMs is a plus.  \n Machine learning background in Python; experience with PyTorch or TensorFlow preferred.\n Ability to communicate technical details to nontechnical stakeholders\n You keep the mission in mind, take ideas and help them grow using data and rigorous testing, show evidence of progress and then double down\n Desire for impact with a growth-minded and collaborative mindset\n \n \n Data Retention, Privacy \u0026 Bias Audit: Data collected during this process is retained in accordance with our Candidate Privacy Policy and applicable state laws. In compliance with New York City Local Law 144, the independent bias audit summary for Gem is publicly available for review at our Careers Page . \n Notice to Applicants for Jobs Located in NYC or Remote Jobs Associated With Office in NYC Only\n We use Covey as part of our hiring and/or promotional process for jobs in NYC and certain features may qualify it as an AEDT in NYC. As part of the hiring and/or promotion process, we provide Covey with job requirements and candidate submitted applications. We began using Covey Scout for Inbound from August 21, 2023, through December 21, 2023, and resumed using Covey Scout for Inbound again on June 29, 2024.\n The Covey tool has been reviewed by an independent auditor. Results of the audit may be viewed here: Covey \n Compensation \n The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares about you and your overall well-being. That’s why we offer a comprehensive benefits package to all regular employees, which includes a 401(k) plan with employer matching, 16 weeks of paid parental leave, wellness benefits, commuter benefits match, paid time off and paid sick leave in compliance with applicable laws (e.g. Colorado Healthy Families and Workplaces Act). DoorDash also offers medical, dental, and vision benefits, 11 paid holidays, disability and basic life insurance, family-forming assistance, and a mental health program, among others.\n To learn more about our benefits, visit our careers page here .\n See below for paid time off details:\n \n For salaried roles: flexible paid time off/vacation, plus 80 hours of paid sick time per year.\n For hourly roles: vacation","salary_min":203500,"salary_max":299300,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","cloud","deep-learning","fine-tuning","healthcare","pytorch","tensorflow","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8225006","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T17:31:26Z","expires_at":"2026-11-13T17:03:28.796671Z","created_at":"2026-10-14T15:25:31.438732Z","updated_at":"2026-10-14T17:03:28.799759Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/634e56a9-b38d-474b-847e-3dc417b38339"},{"id":"501a4fae-853d-480b-9dbb-39daa44f8047","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Staff Machine Learning Engineer, Fulfillment Planning","slug":"staff-machine-learning-engineer-fulfillment-planning-53570b60","description":"About the Team \n The Fulfillment Planning team builds the intelligence that powers DoorDash’s logistics network. We optimize how deliveries are planned and executed across the full delivery lifecycle, improving customer experience, merchant outcomes, Dasher efficiency, and DoorDash profitability.  Our mission is to improve fulfillment quality while reducing fulfillment cost. We do this by applying machine learning, optimization, and systems engineering to the core decisions behind assignment, routing, batching, timing, and fulfillment estimation.\n The team works on some of DoorDash’s most important logistics systems, including:\n \n The core assignment engine that matches deliveries with Dashers in real time.\n Real-time ETA and fulfillment estimation systems for consumers, Dashers, and merchants across diverse geographies and all business lines.\n Assignment and planning algorithms for specialized delivery types, including grocery, retail, parcel, and catering.\n ML models and optimization algorithms that shape demand, improve service quality, and reduce cost.\n Tier-0 logistics services that require high reliability, low latency, and strong operational discipline.\n \n The team also builds reusable ML systems and modeling patterns that scale across DoorDash’s logistics ecosystem. This role will help define the technical direction and best practices for logistics ML at DoorDash.\n About the Role \n We’re looking for a Staff Machine Learning Engineer to lead the design, development, and deployment of large-scale production ML systems that drive real-time decisioning across DoorDash’s fulfillment ecosystem.\n You will start by owning ML systems for assignment and fulfillment estimation, partnering closely with Product, Data Science, Engineering, and Platform teams to improve delivery quality, cost, and efficiency. Over time, you may also contribute to adjacent areas such as batching, fulfillment execution, demand shaping, and logistics optimization across DoorDash’s business lines.\n This is a high-impact individual contributor role for someone who enjoys building 0→1 ML systems, operating at Staff-level scope, and influencing technical direction across multiple teams. You will define architectures, set modeling and deployment standards, mentor other engineers, and help shape how DoorDash applies machine learning to logistics at scale.\n You’re excited about this opportunity because you will… \n \n Own and build foundational ML systems that directly impact delivery quality, cost, and overall logistics efficiency across DoorDash.\n Work on challenging, real-world machine learning problems , including real-time assignment, routing, and fulfillment estimation.\n Lead 0→1 ML initiatives , defining how machine learning and optimization are applied across fulfillment products.\n Influence architecture, strategy, and execution for a Tier-0 service critical to DoorDash’s logistics platform.\n Collaborate closely with Product, Data Science, and Platform Engineering in a highly cross-functional environment.\n Establish best practices for model development, deployment, monitoring, retraining, and governance.\n Define and lead DoorDash’s cutting-edge AI vision for logistics: an LLM-inspired foundation model for intelligence across logistics\n Mentor other engineers and raise the technical bar for logistics ML across the organization.\n \n We’re excited about you because… \n \n You have 8+ years of industry experience building and deploying production-scale machine learning systems.\n You have strong machine learning fundamentals and know how to apply them to large-scale production systems.\n You are fluent in Python\n You have hands-on experience with modern ML frameworks, especially deep learning frameworks.\n You have designed, launched, and operated mission-critical ML models or systems in production, including monitoring, retraining, reliability, and governance.\n You can lead complex technical projects end to end and influence stakeholders across multiple teams or organizations.\n You communicate clearly with both technical and non-technical audiences.\n You are comfortable operating in ambiguous problem spaces and turning 0→1 ideas into production systems.\n You have built or shipped large-scale ML models for recommendation, ads, marketplace, logistics, or other domains.\n You have experience with knowledge distillation from large teacher models into efficient production models.\n Compensation \n The successful candidate’s starting pay will fall within the pay range listed below and is determined based on job-related factors including, but not limited to, skills, experience, qualifications, work location, and market conditions. Base salary is localized according to an employee’s work location. Ranges are market-dependent and may be modified in the future.\n In addition to base salary, the compensation for this role includes opportunities for equity grants. Talk to your recruiter for more information.\n DoorDash cares ","salary_min":203500,"salary_max":299300,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["fine-tuning","healthcare","cloud","generative-ai","llm","deep-learning","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8224336","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T17:30:37Z","expires_at":"2026-11-13T17:03:28.79242Z","created_at":"2026-10-14T15:25:31.434336Z","updated_at":"2026-10-14T17:03:28.795093Z","company_name":"DoorDash","company_slug":"doordash","company_logo_url":"https://www.google.com/s2/favicons?domain=doordash.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/501a4fae-853d-480b-9dbb-39daa44f8047"},{"id":"fcb2f68d-9ea2-439d-9d09-29431b4bdeec","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Machine Learning Engineer - Gen SWE","slug":"machine-learning-engineer-gen-swe-049769f2","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 This role follows a hybrid work schedule and reports to a Software Tech Lead Manager.\n You will: \n \n \n Own and drive the roadmap for ML-powered pickup and dropoff features, from problem definition through launch, in partnership with PM, UX, data science, and other commercialization engineering teams\n \n Train, evaluate, and deploy ML models for occupancy detection, rider-vehicle interactions, pullover selection, and lost and found\n \n Design fallback systems that cascade between onboard and offboard models based on latency, confidence, and connectivity\n \n Build distributed systems that span vehicles, cloud services, and rider apps, and that handle intermittent and inconsistent connectivity well\n \n Work across the stack (data pipelines, model serving, backend services, and client integration) to ship end-to-end, user-facing features\n \n Define success metrics and use offline evaluation, simulation, and live experiments to measure impact on service quality and rider experience\n \n You have: \n \n \n BS degree in Computer Science or equivalent practical experience\n \n 5+ years of software engineering experience, including building and deploying ML models into production\n \n Experience building backend distributed systems\n \n We prefer: \n \n \n Experience with C++\n \n MS in Computer Science, or equivalent practical experience\n \n Experience in developing machine learning models, infrastructure, and pipelines\n \n Experience deploying models on-device or on edge hardware with compute and latency constraints\n \n Track record of owning ambiguous, user-facing problems end-to-end and setting your own technical direction\n \n Experience in autonomous vehicles or robotics\n \n Disclosure for WA Based \u0026 Remote Roles: \n In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include:\n Health, dental, vision, life, disability insuranceRetirement Benefits: 401(k) with company matchPaid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employmentSick Time: 40 hours/year (statutory, where applicable); 5 days/event (discretionary) Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks Baby Bonding Leave: 18 weeks Holidays: 13 paid days per year\n US Remote Only: Please note that Waymo may not be able to employ remotely in all US locations. Please speak with your recruiter about your preferred location for remote work when you begin the interview process.\n  \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 $213,000 — $263,000 USD","salary_min":213000,"salary_max":263000,"location":"Mountain View, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["mlops","distributed-systems","data-pipeline","autonomous-vehicles","robotics","machine-learning"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=8212440","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-13T13:20:32Z","expires_at":"2026-11-13T16:55:54.938681Z","created_at":"2026-10-14T15:20:04.385328Z","updated_at":"2026-10-14T16:55:54.941384Z","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/fcb2f68d-9ea2-439d-9d09-29431b4bdeec"},{"id":"6aceb6c9-aa17-4cd3-81ca-d26284538246","company_id":"5b08d89d-9846-4ef7-8b53-d07ef3ee6c19","title":"Software Engineer, ML Infrastructure","slug":"software-engineer-ml-infrastructure-f2fd5868","description":"About Arc Institute \n Arc Institute is an independent nonprofit research organization at the interface of artificial intelligence and biology, working to accelerate scientific progress and understand the root causes of complex diseases. Founded in 2021 and based in Palo Alto, Arc partners with Stanford University, UC Berkeley, and UC San Francisco.\n Unlike academia, our scientists have long-term funding and industry-like resources. Unlike industry, they're free to pursue high-risk, long-term research without commercial pressures. Arc's Technology Centers and Core Investigator labs work side by side, integrating experimental and computational biology under one roof to tackle problems neither could solve alone.\n Our two Institute Initiatives reflect this model in action:\n \n Virtual Cell Initiative : Building a full-stack virtual cell model to identify disease mechanisms and nominate drug targets,  accelerating the path from biological insight to clinical trials.\n Alzheimer's Disease Initiative : Mapping the genes, pathways, and environmental factors behind Alzheimer's disease to develop drug candidates that address root causes.\n \n More than 300 Arconauts work together at our Palo Alto headquarters, backed by substantial long-term philanthropic funding.\n About the position \n You'll join Arc's Infrastructure Engineering team and own the data and evaluation of ML infrastructure for Arc's virtual cell models. You'll start with two systems: our Model Performance Dashboard, which benchmarks those models, and the pipeline that turns single-cell data into their training sets. You'll set the technical direction for both and connect them, so every new dataset version is judged by what it does to the models. Later in your first year, the role widens into the engineering that makes the ML team's training and evaluation run fast and at scale, lab-in-the-loop infrastructure, validation frameworks for agent-written software, and Arc's rules for what research data we bring in, keep and share. The role consolidates ownership that is split across several teams today. \n Why this position could be the best job you’ve ever had \n \n You will contribute to building a virtual cell. Your evals show which data makes Arc's models better, and the answer shapes which experiments Arc's labs run next.\n You will own the platform end to end, and you work directly with the ML researchers and scientists who rely on it.\n \n About you \n \n You finish what you own, and you fix what's broken without being asked.\n You're curious about the biology, enough to learn single-cell perturbation data and argue about what an eval should measure. \n You're steady when priorities shift. Research is high-risk, so you test ideas early and change course when the evidence says to.\n You don't wait on scarce expert time. If proving a dataset's value means running the model evals yourself, you run them.\n \n In this position you will \n \n Own how Arc measures the performance of its virtual cell models, starting with the backend of our Model Performance Dashboard, which you'll take over within your first two months. \n Decide with the ML team which evaluation tools they need, then design and build them so researchers can evaluate and compare models themselves, and measure what each dataset version does to model performance.\n Redesign the pipeline that turns single-cell data into training sets for our virtual cell models, then run it with AI agents: agents find, process and curate datasets, and automated quality checks, not manual review, decide whether a dataset version is ready for model training.\n Own the lab-in-the-loop infrastructure that turns model performance results into the case for what Arc's labs should test next, working directly with the science leads who design those experiments . \n Build or buy validation frameworks (tests, evals, QC gates, policies) that let agent-written code and apps ship without line-by-line human review. Start with our team, where you'll also own how we work with coding agents, then extend them to apps that scientists across Arc build themselves.\n Own the engineering that makes the ML team's training and evaluation run fast, reliably and at scale. The ML team owns the research and the models. Once a model is ready to push, it enters the train-and-eval CI system you own, which feeds our Model Performance Dashboard. You'll also make multi-node training a supported path, track down distributed-training failures, and measure and raise throughput and GPU utilization (MFU) on clusters that Arc IT operates.\n \n Requirements \n \n Bachelor’s degree with 8 years of relevant experience, or Master’s degree with 6 years of relevant experience.\n You've owned a production data or ML platform that researchers depend on, end to end (pipelines, storage, versioning), and set its technical direction.\n You design and build systems with AI tools and agents, and you make their output verifiable with evals, QC gates or tests before anyone relies on it.\n ","salary_min":191000,"salary_max":237000,"location":"Palo Alto, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","healthcare","pytorch","distributed-systems","agents","machine-learning","infrastructure"],"apply_url":"https://job-boards.greenhouse.io/arcinstitute/jobs/6222681004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-12T22:16:26Z","expires_at":"2026-11-13T17:03:53.841883Z","created_at":"2026-10-14T15:25:57.647484Z","updated_at":"2026-10-14T17:03:53.845803Z","company_name":"Arc Institute","company_slug":"arc-institute","company_logo_url":"https://www.google.com/s2/favicons?domain=arcinstitute.org\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/6aceb6c9-aa17-4cd3-81ca-d26284538246"},{"id":"84dcc4a4-8758-4ae4-a08c-63d080d8c66e","company_id":"b467c425-56b3-40ce-826a-e603e82a08bd","title":"Principal Machine Learning Platform Engineer, Foundation AI ","slug":"principal-machine-learning-platform-engineer-foundation-ai-dcf9ae0b","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 ML Platform powers hundreds of use cases and billions of inferences per day across Discovery, Safety, Economy, and Creation. We build the primitives that let teams train, evaluate, deploy, and operate models quickly and safely—so a new ML idea can reach production in weeks or less. We’re looking for a Principal Platform Engineer who treats platform as a product : someone who can turn complex ML/AI infrastructure into clear, durable APIs, SDKs and easy-to-use CLIs/UIs that our internal developers love.\n This role blends product thinking, developer experience, backend engineering, and infrastructure at scale. Hands‑on ML experience is a plus; a track record of building internal platforms that developers love is a must.\n You Are \n \n Have 10+ years of professional experience and have a wealth of system design experience upon which to draw to build a scalable, reliable ML platform for all of Roblox.\n A Code Machine; you love not only to design and communicate ideas but also to actually ship product. You obsess about user feedback, and constantly drive towards getting platform features in customers hands.\n Proficient in API design and developer experience—gRPC/REST APIs, SDKs, CLIs, and simple UIs that developers love to use. \n Good to have - experience with the end‑to‑end ML model lifecycle such as model serving, training, model CI/CD, and GPU resources management, and have built ML platform features that are delightful to use. \n You're passionate about infrastructure‑as‑code and automating painful manual processes. You push for platform solutions that don’t leave on-call teams carrying the burden of design choices.\n Passionate about supporting ML engineers to meet and understand their needs, and translating them into clean, durable platform abstractions.\n A clear communicator who excels at both written and verbal communication across differing levels of technical detail. \n Bachelor's degree in Computer Science, Computer Engineering, Data Science, or a similar technical field.\n \n You Will \n \n Own platform as a product and set direction end to end: Define requirements, write RFDs, and ship APIs, SDKs, CLIs, and UIs that make ML@Roblox easy to adopt.\n Bootstrap and maintain core ML Platform components: Serving Layer, Model Registry, Pipeline Orchestrator, and Training/Inference control planes.\n Set technical strategy and oversee development of high scale and reliable infrastructure systems, with clear SLOs for latency, availability, and cost. \n Design great developer experiences with paved‑road templates, golden paths, opinionated defaults, and clear docs to reduce time‑to‑first‑production. \n Instrument the platform to measure adoption, friction, reliability, and cost; use data to prioritize roadmap and validate outcomes. \n Partner across organizations (ML Engineering, Data Science, Infra/SRE, Security, Finance) to optimize performance, safety, and spend, especially for GPU‑intensive training and high‑QPS inference. \n Propose and implement new platform tooling to improve time to production for MLEs across the full ML lifecycle.\n Stay abreast of industry trends in machine learning and infrastructure to ensure the adoption of leading‑edge technologies and practices. \n Mentor junior and senior engineers, lead design reviews, and drive cross‑team architectural decisions that last.\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 $295,250 — $345,050 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 e","salary_min":295250,"salary_max":345050,"location":"San Mateo, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["mlops","api-design","platform","machine-learning"],"apply_url":"https://careers.roblox.com/jobs/8233722?gh_jid=8233722","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-12T20:57:31Z","expires_at":"2026-11-13T17:02:53.15447Z","created_at":"2026-10-14T15:24:56.150214Z","updated_at":"2026-10-14T17:02:53.157398Z","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/84dcc4a4-8758-4ae4-a08c-63d080d8c66e"},{"id":"32e747ed-8b79-450b-9035-c6baf08039fa","company_id":"82d2abc2-444c-4d89-9646-4739e72d700d","title":"Senior Machine Learning Engineer","slug":"senior-machine-learning-engineer-2cccd4cc","description":"About Checkr Checkr is building the data platform to power safe and fair decisions. Over 140,000 companies and millions of people rely on Checkr for AI verification in the moments that matter most: getting a new job, a new place to live, a car ride, childcare, even a date. Customers include Uber, Pennymac, Airbnb, Doordash, and Anthropic. We’re a team that thrives on solving complex problems with innovative solutions that advance our mission. Checkr is recognized on Forbes Cloud 100 2025 List and is a Y Combinator 2024 Breakthrough Company .\n About the team/role \n We’re hiring an ML Engineer (P3) to build and ship the AI systems that power Checkr’s core products. This role sits on the ML team inside Checkr’s Data \u0026 ML organization within Engineering.\n Checkr runs millions of background checks a year. The ML team builds the systems that make those checks faster, more accurate, and cheaper to operate: document processing, charge classification, entity resolution, and in-product intelligence. These are production services that Product Engineering depends on daily.\n This is not a research role or a notebook role. You’ll own ML services end-to-end: design them, code them, deploy them, monitor them. We need someone who writes production software, builds with LLMs and APIs as first-class tools, and can tell the difference between working code and AI slop. If you’ve spent the last few years building AI-native software and you care deeply about engineering craft, we want to talk.\n This role sits in the central Data \u0026 ML team within the Engineering organization. You will partner daily with Product Engineering, Product, and cross-functional teams. You’ll also contribute to Checkr’s broader AI strategy, including our initiative to deploy our agentic fleet and build scalable context with our semantic layer.\n We are looking for someone based in San Francisco who has built ML systems in fast-moving, impact-first environments. Less process, more shipping. Less paperwork, more results.\n What you’ll do \n \n Build and deploy ML/AI services. Design, develop, and ship ML models and AI systems that Product Engineering teams rely on. You write the model code, the API layer, the monitoring, and the tests. Not notebooks; production services.\n Design with LLMs and APIs. Use LLM APIs (OpenAI, Anthropic, etc.) as building blocks in production systems. You know when to call an LLM, when to fine-tune, when to use a classical model, and when to write a rule. You think about cost, latency, and quality together.\n Ship production software. Write clean, well-structured code with solid OOP, proper abstractions, error handling, and tests. Your code gets reviewed by SWEs and passes. CI/CD is how you work, not something you bolt on at the end.\n Partner with product and engineering. Translate business problems into ML solutions. Define API contracts with product engineers. Explain your approach clearly to non-ML partners and leave the room with alignment, not confusion.\n Evaluate and iterate fast. Build evaluation frameworks, run experiments, and make data-driven decisions about model and system performance. Ship and iterate; don’t wait for perfect.\n Ship AI-powered workflows. Put AI to work on your own processes: automate pipelines, build agentic workflows, and contribute reusable skills and context to Checkr’s agentic platform. The expectation is that our teams operate AI-first.\n \n  \n What you bring \n \n A Bachelor’s or Master’s degree in Computer Science, Mathematics, or a related technical field, or equivalent depth from experience\n 6+ years building software professionally, with at least 2 of those building ML systems that run in production\n Strong Python fluency; you write clean, testable, well-structured code with solid OOP instincts. Hands-on experience using LLM APIs in production systems: prompt engineering, structured outputs, function calling, cost management, and evaluation\n You’ve built and maintained APIs, worked with CI/CD pipelines, and shipped code that other engineers depend on\n Comfort with and enthusiasm for AI-assisted workflows; experience using LLMs, code-generation tools, or agentic systems in production or operational contexts is a strong signal\n You use AI tools (Copilot, Claude, etc.) to move faster, but you understand every line they produce. You can spot AI slop and you don’t ship it\n An A-player mindset with a strong bias for action: you raise the bar, move with urgency, stay resilient through ambiguity, and take ownership to deliver meaningful outcomes. \n \n Nice to have \n \n Experience with MLOps platforms (MLflow, SageMaker, Vertex, or similar)\n Background in document processing, OCR, or information extraction\n Experience with PySpark or large-scale data processing\n Ruby experience (Checkr’s platform runs on Rails)\n Familiarity with compliance-sensitive domains (fintech, legal tech, HR tech)\n Working knowledge of dbt, Snowflake, or modern ELT/data transformation tools\n Pay Transparen","salary_min":176000,"salary_max":207000,"location":"Denver, CO","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","code-generation","payments","legal","llm","mlops","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/checkr/jobs/8274246","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-12T16:04:33Z","expires_at":"2026-11-13T16:58:42.655818Z","created_at":"2026-10-14T15:22:20.04347Z","updated_at":"2026-10-14T16:58:42.658798Z","company_name":"Checkr","company_slug":"checkr","company_logo_url":"https://www.google.com/s2/favicons?domain=checkr.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/32e747ed-8b79-450b-9035-c6baf08039fa"},{"id":"362dd4a4-f865-4d4a-9a7a-bc7e1b3dff50","company_id":"861968d1-d9f8-4217-9873-ce4b24851abc","title":"Staff Machine Learning and Bioinformatics Scientist (Multi-Cancer Early Detection)","slug":"staff-machine-learning-and-bioinformatics-scientist-multi-cancer-early-detection-df2fad4b","description":"Natera’s Research team is building tests that can detect many cancers from a single blood draw. We are looking for a Staff Machine Learning and Bioinformatics Scientist to help design the models at the heart of that effort. You’ll work with large-scale, multi-modal genomic datasets from clinical cohorts and partner closely with molecular biologists and clinical scientists. Your research will advance the state-of-the-art and you will drive new methods from prototype, through scalable implementation, toward clinical validation and, ultimately, to patients.\n PRIMARY RESPONSIBILITIES: \n \n Lead the development of novel statistical and machine learning methods for cancer signal detection on multi-modal genomic data\n Build interpretable, well-calibrated models, setting the standard for uncertainty estimation and reproducibility across the group\n Prototype, test and productionize ML models using scalable infrastructure\n Own key technical deliverables within larger projects and help shape the team’s research roadmap\n Collaborate closely with molecular biologists and clinical experts on design and analysis of experiments and validation studies\n Keep raising the bar through code and design reviews across projects\n Translate complex results into clear stories for technical and non-technical audiences\n Mentor junior scientists and champion a culture of curiosity, collaboration, and scientific excellence\n \n QUALIFICATIONS: \n \n PhD in Computer Science, Mathematics, Bioinformatics, or a related quantitative field with 6-12 years of experience post-PhD\n Hands-on experience developing novel statistical / ML analysis methods applied to complex biological data\n Proven track record of innovation and technical leadership in complex method development projects\n Expertise in deep learning and multimodal foundation models\n Proficient in Python and its scientific computing ecosystem\n Scientific rigor throughout the R\u0026D process, with eagerness to teach and learn new computational methods and biology\n Excellent communication skills and a collaborative approach across teams\n \n Preferred Experience: \n \n Familiarity with DNA methylation and other epigenomic data\n Experience with cell-free DNA and liquid biopsy samples\n Background in cancer genomics and diagnostics\n Hands-on work with multimodal foundation models applied to biological data\n The pay range is listed and actual compensation packages are based on a wide array of factors unique to each candidate, including but not limited to skill set, years \u0026 depth of experience, certifications and specific office location. This may differ in other locations due to cost of labor considerations.\n Remote USA\n $163,200 — $204,000 USD \n OUR OPPORTUNITY \n Natera™ is a global leader in cell-free DNA (cfDNA) testing, dedicated to oncology, women’s health, and organ health. Our aim is to make personalized genetic testing and diagnostics part of the standard of care to protect health and enable earlier and more targeted interventions that lead to longer, healthier lives.\n The Natera team consists of highly dedicated statisticians, geneticists, doctors, laboratory scientists, business professionals, software engineers and many other professionals from world-class institutions, who care deeply for our work and each other. When you join Natera, you’ll work hard and grow quickly. Working alongside the elite of the industry, you’ll be stretched and challenged, and take pride in being part of a company that is changing the landscape of genetic disease management.\n WHAT WE OFFER \n Competitive Benefits - Employee benefits include comprehensive medical, dental, vision, life and disability plans for eligible employees and their dependents. Additionally, Natera employees and their immediate families receive free testing in addition to fertility care benefits. Other benefits include pregnancy and baby bonding leave, 401k benefits, commuter benefits and much more. We also offer a generous employee referral program!\n For more information, visit www.natera.com .\n Natera is proud to be an Equal Opportunity Employer. We are committed to ensuring a diverse and inclusive workplace environment, and welcome people of different backgrounds, experiences, abilities and perspectives. Inclusive collaboration benefits our employees, our community and our patients, and is critical to our mission of changing the management of disease worldwide.\n All qualified applicants are encouraged to apply, and will be considered without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, age, veteran status, disability or any other legally protected status. We also consider qualified applicants regardless of criminal histories, consistent with applicable laws.\n If you are based in California, we encourage you to read this important information for California residents.  \n Link: https://www.natera.com/notice-of-data-collection-california-residents/ \n Please be","salary_min":163200,"salary_max":204000,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"lead","tags":["generative-ai","deep-learning","healthcare","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/natera/jobs/6220885004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-09T22:10:48Z","expires_at":"2026-11-13T16:58:38.001158Z","created_at":"2026-10-14T15:22:15.63645Z","updated_at":"2026-10-14T16:58:38.003762Z","company_name":"Natera","company_slug":"natera","company_logo_url":"https://www.google.com/s2/favicons?domain=natera.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/362dd4a4-f865-4d4a-9a7a-bc7e1b3dff50"},{"id":"51543c6e-0d4b-4d56-b403-d772dc9cc157","company_id":"861968d1-d9f8-4217-9873-ce4b24851abc","title":"Staff Machine Learning and Bioinformatics Scientist (Multi-Cancer Early Detection)","slug":"staff-machine-learning-and-bioinformatics-scientist-multi-cancer-early-detection-bbaf4d4a","description":"Natera’s Research team is building tests that can detect many cancers from a single blood draw. We are looking for a Staff Machine Learning and Bioinformatics Scientist to help design the models at the heart of that effort. You’ll work with large-scale, multi-modal genomic datasets from clinical cohorts and partner closely with molecular biologists and clinical scientists. Your research will advance the state-of-the-art and you will drive new methods from prototype, through scalable implementation, toward clinical validation and, ultimately, to patients.\n PRIMARY RESPONSIBILITIES: \n \n Lead the development of novel statistical and machine learning methods for cancer signal detection on multi-modal genomic data\n Build interpretable, well-calibrated models, setting the standard for uncertainty estimation and reproducibility across the group\n Prototype, test and productionize ML models using scalable infrastructure\n Own key technical deliverables within larger projects and help shape the team’s research roadmap\n Collaborate closely with molecular biologists and clinical experts on design and analysis of experiments and validation studies\n Keep raising the bar through code and design reviews across projects\n Translate complex results into clear stories for technical and non-technical audiences\n Mentor junior scientists and champion a culture of curiosity, collaboration, and scientific excellence\n \n QUALIFICATIONS: \n \n PhD in Computer Science, Mathematics, Bioinformatics, or a related quantitative field with 6-12 years of experience post-PhD\n Hands-on experience developing novel statistical / ML analysis methods applied to complex biological data\n Proven track record of innovation and technical leadership in complex method development projects\n Expertise in deep learning and multimodal foundation models\n Proficient in Python and its scientific computing ecosystem\n Scientific rigor throughout the R\u0026D process, with eagerness to teach and learn new computational methods and biology\n Excellent communication skills and a collaborative approach across teams\n \n Preferred Experience: \n \n Familiarity with DNA methylation and other epigenomic data\n Experience with cell-free DNA and liquid biopsy samples\n Background in cancer genomics and diagnostics\n Hands-on work with multimodal foundation models applied to biological data\n \n Compensation \u0026 Total Rewards  \n This range reflects a good-faith estimate of the base pay we reasonably expect to offer at the time of  hire. Final compensation will vary based on experience, qualifications, and internal equity considerations. \n This position is also eligible for additional compensation and benefits through Natera’s robust Total Rewards program, including: \n \n \n Annual performance incentive bonus \n \n Long-term equity awards \n \n Comprehensive health benefits (medical, dental, vision) \n \n 401(k) with company match \n \n Generous paid time off and company holidays \n \n Additional wellness and work-life benefits \n \n \n Compensation Range \n $169,800 — $212,300 USD \n OUR OPPORTUNITY \n Natera™ is a global leader in cell-free DNA (cfDNA) testing, dedicated to oncology, women’s health, and organ health. Our aim is to make personalized genetic testing and diagnostics part of the standard of care to protect health and enable earlier and more targeted interventions that lead to longer, healthier lives.\n The Natera team consists of highly dedicated statisticians, geneticists, doctors, laboratory scientists, business professionals, software engineers and many other professionals from world-class institutions, who care deeply for our work and each other. When you join Natera, you’ll work hard and grow quickly. Working alongside the elite of the industry, you’ll be stretched and challenged, and take pride in being part of a company that is changing the landscape of genetic disease management.\n WHAT WE OFFER \n Competitive Benefits - Employee benefits include comprehensive medical, dental, vision, life and disability plans for eligible employees and their dependents. Additionally, Natera employees and their immediate families receive free testing in addition to fertility care benefits. Other benefits include pregnancy and baby bonding leave, 401k benefits, commuter benefits and much more. We also offer a generous employee referral program!\n For more information, visit www.natera.com .\n Natera is proud to be an Equal Opportunity Employer. We are committed to ensuring a diverse and inclusive workplace environment, and welcome people of different backgrounds, experiences, abilities and perspectives. Inclusive collaboration benefits our employees, our community and our patients, and is critical to our mission of changing the management of disease worldwide.\n All qualified applicants are encouraged to apply, and will be considered without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, age, veteran status, disability","salary_min":169800,"salary_max":212300,"location":"San Carlos, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["generative-ai","deep-learning","healthcare","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/natera/jobs/6221940004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-09T22:10:46Z","expires_at":"2026-11-13T16:58:37.997051Z","created_at":"2026-10-14T15:22:15.640367Z","updated_at":"2026-10-14T16:58:37.999729Z","company_name":"Natera","company_slug":"natera","company_logo_url":"https://www.google.com/s2/favicons?domain=natera.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/51543c6e-0d4b-4d56-b403-d772dc9cc157"},{"id":"943a5e5f-5b9a-4c60-ae1f-a9319c87ec0e","company_id":"4bc4e268-7a05-4a65-a162-1688af546f7e","title":"Senior Machine Learning Programmer","slug":"senior-machine-learning-programmer-dd417989","description":"WHAT MAKES US EPIC?\n At the core of Epic’s success are talented, passionate people. Epic prides itself on creating a collaborative, welcoming, and creative environment. Whether it’s building award-winning games or crafting engine technology that enables others to make visually stunning interactive experiences, we’re always innovating.\n Being Epic means being a part of a team that continually strives to do right by our community and users. We’re constantly innovating to raise the bar of engine and game development.\n PROGRAMMING - SPECIAL PROJECTS\n What We Do \n The Special Projects team at Epic is responsible for executing high-impact projects that push the envelope to define the future of real-time graphics and gaming technology (The Matrix Awakens, Lumen in the Land of Nanite). In collaboration with the Unreal Engine team, we strive to put our technology and knowledge into the hands of users, empowering developers and content creators with the most powerful suite of real-time tools in the world.\n What You'll Do \n You will work with our team of applied research programmers at the intersection of computer vision, gameplay agents, natural language processing, and machine learning to create state-of-the-art tools to support game developers in our ecosystem with a focus on bots (trained gameplay agents, intelligent NPCs, companions, world modeling), as well as automation of a number key areas (including moderation, security, and QA testing). You will be responsible for the design, implementation and deployment of production-ready machine learning models that can be integrated in our ecosystem.\n In this role, you will \n \n Create and deliver production-ready, scalable and high-quality machine learning models and associated algorithms that can make actions in game environments\n Critically assess the effectiveness of such models and make recommendations for the ongoing roadmap\n Methodically understand and assess the effectiveness of new data channels and support collection/creation of new ones\n \n What we're looking for \n \n PhD in Computer Science, Mathematics or related field, or 3+ years of relevant industry experience\n Experience creating / applying machine learning algorithms for action modeling of bots behavior, vision-based or language-instructed, and deploying them as production-level systems in-game\n Expert, hands-on knowledge of:\n \n Deep learning, incl. but not limited to applications in the gaming industry\n Foundational models and vision and/or language, especially vision-language-action models, reinforcement learning agents, incl. local deployment and model post-training of open weights models\n Reinforcement learning, incl. experience with agentic systems, reward design, policy optimisation, evaluation, and building scalable training environments for agents\n Python and associated ML tools/frameworks (numpy, scipy, sklearn, pytorch)\n \n Experience with working collaboratively with other developers, following a flexible/agile/lean approach\n \n This role is open to multiple locations across North America \u0026 Europe (including NYC). \n Pay Transparency Information \n The expected annual base pay range(s) for this position are detailed below. Each base pay range is relevant only for individuals who are residents of or will be expected to work within the specified locale. Compensation varies based on a variety of factors, which include (but aren’t limited to) things such as skills and competencies, qualifications, knowledge, and experience. In addition to base pay, most employees are eligible to participate in Epic’s generous benefit plans and discretionary incentive programs (subject to the terms of those plans or programs). \n New York City Base Pay Range\n $232,666 — $341,243 USD \n ABOUT US\n Epic Games​ ​is a leading interactive entertainment company. For over 30 years we've been making award-winning games and engine technology that empowers others to make visually stunning games and 3D content that bring environments to life like never before. Epic's award-winning Unreal Engine technology not only provides game developers the ability to build high-fidelity, interactive experiences for PC, console, mobile, and VR, it is also a tool being embraced by content creators across a variety of industries such as media and entertainment, automotive, and architectural design. As we continue to build our Engine technology and develop remarkable games, we strive to build teams of world-class talent.\n Like what you hear? Come be a part of something Epic! \n Epic Games deeply values diverse teams and an inclusive work culture, and we are proud to be an Equal Opportunity employer. Learn more about our Equal Employment Opportunity (EEO) Policy here .\n Note to Recruitment Agencies: Epic does not accept any unsolicited resumes or approaches from any unauthorized third party (including recruitment or placement agencies) (i.e., a third party with whom we do not have a negotiated and validly executed agreement). We will ","salary_min":232666,"salary_max":341243,"location":"BLANK,BLANK,Multiple Locations","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","pytorch","deep-learning","reinforcement-learning","nlp","computer-vision","machine-learning"],"apply_url":"https://epicgames.com/careers/jobs/6221662004?gh_jid=6221662004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-09T19:46:26Z","expires_at":"2026-11-13T17:02:44.874623Z","created_at":"2026-10-14T15:24:47.963173Z","updated_at":"2026-10-14T17:02:44.878058Z","company_name":"Epic Games","company_slug":"epic-games","company_logo_url":"https://www.google.com/s2/favicons?domain=epicgames.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/943a5e5f-5b9a-4c60-ae1f-a9319c87ec0e"},{"id":"58d65f8c-9bfe-4e0f-8026-f55da835720b","company_id":"26618e2f-35c7-42eb-8f60-bd25a7e9a0d2","title":"Staff Machine Learning Engineer","slug":"staff-machine-learning-engineer-9c39e036","description":"Ready to do the most impactful work of your career? At  Coinbase , we are uncompromising on our mission to increase economic freedom. The bar is high, the environment is intense, and we like it that way. This isn't a place for complacency, it’s a place to be pushed past your perceived limits. If you're ready to build the future of finance alongside people who refuse to settle for \"good enough,\" you belong here. Coinbase is a remote-first, but not remote-only company. Expect to get together quarterly for intense in-person working sessions called “surges.”  learn more about working at Coinbase .\n Staff Machine Learning Engineer, CX Intelligence \n  \n CEE Intelligence (CEEI) is the ML and intelligence layer for Coinbase's customer engagement ecosystem, building customer intelligence, evaluation systems, routing and classification models, safety guardrails, and decisioning capabilities that power Help Center, chat, voice, agent tooling, and other customer support runtimes. As a Staff Machine Learning Engineer, you'll lead the design and delivery of production ML systems across the org, shaping technical strategy, building scalable intelligence capabilities, and partnering with product and engineering stakeholders to improve resolution quality, automation coverage, safety, and customer experience. What you'll do: \n \n Own the design and delivery of production ML systems, applying LLMs, deep learning, graph neural networks, and classical ML techniques to solve complex customer experience problems.\n Partner with leadership and cross-functional stakeholders to define strategic roadmaps and translate vision into quarterly execution plans with measurable outcomes.\n Lead high-impact ML projects end-to-end, from architecture through production, managing priorities, risks, and dependencies.\n Build scalable, secure, and reliable ML systems, evaluation pipelines, and data workflows using high-quality, well-tested Python code.\n Mentor engineers and establish team-wide standards for design, testing, observability, and operational excellence.\n Drive design reviews covering security, responsible AI, model quality, and architectural integrity, improving reliability, scalability, and latency across the stack.\n \n Required Skills and Experience: \n \n 8+ years of experience in machine learning and software engineering, with proven success building, scaling, and maintaining production ML systems, data platforms, or analytical services.\n Hands-on expertise with one or more of LLMs, deep learning, graph neural networks, gradient-boosted trees, or regression, combined with strong knowledge of NLP, information retrieval, data mining, or advanced statistics.\n Strong Python skills with demonstrated ability to write maintainable, highly tested production code for ML services.\n Experience mentoring engineers and raising technical standards across a team or organization, including leading design reviews and influencing technical and non-technical stakeholders.\n Utilizes generative AI responsibly, maintaining human oversight to deliver business-ready outputs and drive measurable improvements in workflow efficiency, cost, and quality.\n \n Job #: P77568\n Pay Transparency Notice: Base salary varies by location (see range below). Total compensation may also include equity and bonus eligibility, and benefits (medical, dental, vision, 401(k)). \n  \n Annual base salary range (excluding equity and bonus):\n $218,025 — $256,500 USD \n \n Application Limit: Candidates may submit a maximum of 3 applications within a 6-month period.\n Equal Opportunity Employer: Coinbase is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, protected veteran status, or genetic information. Applicants with criminal histories will be considered consistent with applicable federal, state, and local laws. \n US Applicants: View Employee Rights , Know Your Rights , and E-Verify Notice of Participation. \n Accommodations: If you are an individual with a disability who needs a reasonable accommodation, email us your request and contact info at accommodations[at]coinbase.com. Need screen reading technology? Click here to download a free compatible screen reader and view the tutorial . \n Data Privacy \u0026 Arbitration: By submitting your application, you agree to our Candidate Privacy Notice . US applicants: By submitting your application, you agree to Arbitration of Disputes .","salary_min":218025,"salary_max":256500,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"lead","tags":["generative-ai","llm","nlp","deep-learning","search","machine-learning"],"apply_url":"https://www.coinbase.com/careers/positions/8267151?gh_jid=8267151","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-09T19:31:10Z","expires_at":"2026-11-13T16:58:01.54146Z","created_at":"2026-10-14T15:21:41.463785Z","updated_at":"2026-10-14T16:58:01.544331Z","company_name":"Coinbase","company_slug":"coinbase","company_logo_url":"https://www.google.com/s2/favicons?domain=www.coinbase.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/58d65f8c-9bfe-4e0f-8026-f55da835720b"},{"id":"e93a2a59-96bf-439e-aeab-74ec34d72a67","company_id":"a0000000-0000-0000-0000-000000000018","title":"Machine Learning Engineer, Trust \u0026 Safety","slug":"machine-learning-engineer-trust-safety-fc325068","description":"About Vercel:\n Vercel is the agentic infrastructure company, freeing people and agents to ship what's next. For more than a decade we've helped builders move from idea to production with speed, security, and exceptional developer experience. \n Now we're scaling our products for both agents and people to ship and run software, built in the open and trusted by OpenAI, PayPal, Ramp, Supreme, and millions of developers worldwide. \n About the Role:\n We are looking for a Machine Learning Engineer on our Trust \u0026 Safety Engineering team, to build and operate the production systems that detect and stop abuse on the platform at internet scale. This is an engineering-first role, roughly 80% writing and shipping production code (services, pipelines, and infrastructure) and 20% modeling, taking detection and classification approaches from prototype to hardened systems. This is a hybrid role based in San Francisco or New York City, with three days a week in the office.\n What you’ll do: \n \n Design, build, and operate production systems that detect and disrupt abusive behavior across the platform, with an emphasis on reliability, scalability, and observability\n Build and maintain machine learning (ML) infrastructure, including training pipelines, feature pipelines, serving infrastructure, and evaluation frameworks\n Ship large language model (LLM) and classical ML approaches for abuse detection and classification, turning prototypes into maintainable code\n Own systems end to end, from integrating a model into the platform through deployment, monitoring, and iteration\n Work with security, product, and infrastructure teams to turn abuse patterns and detection logic into production systems\n \n What you need: \n \n 5+ years of software engineering experience building and operating production systems at scale\n Wrote production code in Python, plus JavaScript/TypeScript or Go\n Built and maintained ML infrastructure, such as training and serving pipelines, feature stores, or evaluation tooling\n Integrated LLMs into production systems, including prompt engineering and evaluation\n \n Bonus if you: \n \n Built an open source ML tool\n Worked in the trust and safety, fraud, or abuse prevention domain\n \n Compensation \u0026 Benefits:\n \n Competitive compensation package, including equity.\n Inclusive Healthcare Package.\n Learn and Grow - we provide mentorship and send you to events that help you build your network and skills.\n Flexible Time Off.\n We will provide you the gear you need to do your role, and a WFH budget for you to outfit your space as needed.\n \n The San Francisco, CA base pay range for this role is $208,000.00 - $312,000.00. Actual salary will be based on job-related skills, experience, and location. Compensation outside of San Francisco may be adjusted based on employee location. The total compensation package may include benefits, equity-based compensation, and eligibility for a company bonus or variable pay program depending on the role. Your recruiter can share more details during the hiring process. \n \n Disclosures: \n \n Privacy: Please review our Job Applicant Privacy Policy for more information on how we handle your data.\n Equal Opportunity:  Vercel is committed to fostering and empowering an inclusive community within our organization. We do not discriminate on the basis of race, religion, color, gender expression or identity, sexual orientation, national origin, citizenship, age, marital status, veteran status, disability status, or any other characteristic protected by law. Vercel encourages everyone to apply for our available positions, even if they don't necessarily check every box on the job description.","salary_min":208000,"salary_max":312000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["llm","healthcare","agents","machine-learning","rust"],"apply_url":"https://job-boards.greenhouse.io/vercel/jobs/6208037004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-09T04:59:47Z","expires_at":"2026-11-13T16:55:41.948472Z","created_at":"2026-10-14T15:19:47.726056Z","updated_at":"2026-10-14T16:55:41.951722Z","company_name":"Vercel","company_slug":"vercel","company_logo_url":"https://www.google.com/s2/favicons?domain=vercel.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/e93a2a59-96bf-439e-aeab-74ec34d72a67"},{"id":"70e145b8-5b41-4b48-a424-a088b204b2a6","company_id":"b467c425-56b3-40ce-826a-e603e82a08bd","title":"[2027] Senior Machine Learning Engineer - PhD Early Career","slug":"2027-senior-machine-learning-engineer-phd-early-career-b6d69cf2","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 At Roblox, we believe advanced research belongs in production, driving core algorithmic advances at a global scale. In this role, you’ll own high-impact projects end-to-end, from initial ideation to production deployment and academic publication, while solving complex, high-dimensional challenges at world-scale concurrency. \n Your expertise, across research areas including Computer Vision and Graphics, multimodality, NLP, or recommendation systems, will directly shape the experience for millions of daily active users. Come build the AI-driven infrastructure powering the future of immersive entertainment.\n You Will \n \n Prototype and implement advanced AI and machine learning solutions that drive core platform features and innovation at scale.\n Develop and deploy cutting-edge models (e.g., deep learning, generative AI, multimodal architectures) to solve complex, high-impact technical challenges.\n Collaborate with applied researchers, engineers, and product teams to translate advanced research concepts into production-ready systems.\n Translate complex research into scalable, robust infrastructure that directly impacts hundreds of millions of daily active users.\n Work backward from user and product needs to architecture, and deliver ML solutions that improve platform engagement, safety, and content ecosystem growth.\n \n You Have \n \n Possess or pursuing a PhD in computer science, engineering, or a related field, with a thesis aligned to Roblox’s research areas.\n Deep expertise and curiosity in a relevant technical domain, such as deep learning, computer vision, NLP, reinforcement learning, or distributed systems.\n Proficiency in one or more programming languages (e.g., Python, C++, Go, Java) and experience building and optimizing large-scale systems.\n A strong research track record, evidenced by multiple publications and presentations in rigorous peer-reviewed venues.\n \n You may redact age, date of birth, and dates of attendance/graduation from your resume.\n As you apply, you can find more information about our process by signing up for Speak_. You'll gain access to our practice assessment, comprehensive guides, FAQs, and modules designed to help you ace the hiring process.\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 $196,750 — $243,290 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":196750,"salary_max":243290,"location":"San Mateo, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["computer-vision","deep-learning","distributed-systems","generative-ai","nlp","reinforcement-learning","machine-learning"],"apply_url":"https://careers.roblox.com/jobs/8242623?gh_jid=8242623","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-08T16:05:13Z","expires_at":"2026-11-13T17:02:53.108743Z","created_at":"2026-10-14T15:24:56.108637Z","updated_at":"2026-10-14T17:02:53.113421Z","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/70e145b8-5b41-4b48-a424-a088b204b2a6"},{"id":"4290888b-e1c9-49a5-9d2a-4ba08900207b","company_id":"714f360f-a244-487d-b3f0-0c43518a9e66","title":"Staff Machine Learning Engineer, Content Visual AI","slug":"staff-machine-learning-engineer-content-visual-ai-1a89c7fb","description":"About Pinterest: \n Millions of people around the world come to our platform to find creative ideas, dream about new possibilities and plan for memories that will last a lifetime. At Pinterest, we’re on a mission to bring everyone the inspiration to create a life they love, and that starts with the people behind the product.\n Discover a career where you ignite innovation for millions, transform passion into growth opportunities, celebrate each other’s unique experiences and embrace the  flexibility to do your best work. Creating a career you love? It’s Possible.\n At Pinterest, AI isn't just a feature, it's a powerful partner that augments our creativity and amplifies our impact, and we’re looking for candidates who are excited to be a part of that. To get a complete picture of your experience and abilities, we’ll explore your foundational skills and how you collaborate with AI.\n Through our interview process, what matters most is that you can always explain your approach, showing us not just what you know, but how you think. You can read more about our AI interview philosophy and how we use AI in our recruiting process here .\n The User and Content Foundations org is responsible for deeply understanding our content, users and their interactions by generating high-quality, reusable signals and insights that power relevance and personalization across the platform. We work at the intersection of distributed machine learning, large multimodal models, data infrastructure, and product adoption to build foundational capabilities that support our partners, including Discovery, Search, Growth, and Ads. We are looking for an experienced Staff Machine Learning Engineer / Technical Lead who can drive the team’s technical direction, lead the team in building scalable content understanding systems, and make an impact on core Pinterest user and business outcomes.  In this role, you will lead ambiguous, high-impact scientific and engineering efforts that create reusable representations, models, and signals.  You’ll work across research, machine learning, infrastructure, and engineering domains to turn advances in VLMs, LLMs, multimodal learning and sequence modeling into impact.  This role is ideal for a deeply technical leader who combines strong ML judgment with product intuition. Expertise in visual representation learning and content understanding is especially valuable; experience with user understanding, recommender systems and LLMs is also highly relevant.\n What will you be doing: \n \n Set the technical direction and multi-quarter strategy for foundational ML capabilities across visual representation learning, multimodal content understanding, and LLM-based personalization.\n Lead high-impact scientific and technical initiatives from problem definition and research strategy through experimentation, productionization, and adoption.\n Build reusable embeddings, representations, semantic signals, and ML infrastructure while advancing techniques such as VLMs, LLMs, sequence modeling, self-supervised learning, retrieval, and distillation.\n Partner with Product, Data Science, Applied Science, and Engineering to translate foundational capabilities into product outcomes and influence cross-functional roadmaps and investments.\n Establish rigorous practices for modeling, data pipelines, distributed training, inference, evaluation, experimentation, and operational excellence while mentoring engineers and scientists.\n \n What we’re looking for: \n \n Minimum 7 years of industry experience, including 2+ years of tech leading teams\n MS or PhD degree in computer science, machine learning, or equivalent industry experience.\n Publications at top machine learning, multimodal and/or data mining conferences (e.g., NeurIPS, ICML, CVPR, ECCV, ACL, KDD, SIGIR, etc.) or open-source ML experience\n Hands-on experience with large-scale distributed training of generative models (LLMs, VLMs, sequence models).\n Hands-on experience with distributed tooling for ML data pipelines (e.g. Spark, Hive, MapReduce)\n Hands-on experience leading ambiguous ML and research efforts in building, applying and improving GenAI models for content understanding, search and recommendation systems.\n Technical leadership and experience in setting direction for roadmaps that span modeling, data pipelines and productionization, and aligning stakeholders on priorities, trade-offs, and execution plans.\n Excellent cross-functional communication and collaboration skills\n \n Relocation Statement: \n \n This position is not eligible for relocation assistance. Visit our PinFlex page to learn more about our working model.\n \n In-Office Requirement Statement: \n \n We recognize that the ideal environment for work is situational and may differ across departments. What this looks like day-to-day can vary based on the needs of each organization or role.\n This role will need to be in the office for in-person collaboration 1-2 times/quarter and can be situated anywhere in the U","salary_min":189308,"salary_max":389753,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"lead","tags":["data-pipeline","distributed-systems","llm","generative-ai","machine-learning"],"apply_url":"https://www.pinterestcareers.com/jobs/?gh_jid=8247246","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-08T15:49:26Z","expires_at":"2026-11-13T16:57:51.232293Z","created_at":"2026-10-14T15:21:31.463492Z","updated_at":"2026-10-14T16:57:51.235425Z","company_name":"Pinterest","company_slug":"pinterest","company_logo_url":"https://www.google.com/s2/favicons?domain=www.pinterest.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/4290888b-e1c9-49a5-9d2a-4ba08900207b"},{"id":"5d06e771-bce8-47ec-ab25-c7a09a6ec39e","company_id":"545e1785-0a1e-478e-b21c-cb7ffa7e0b88","title":"Senior Machine Learning Engineer, Vehicle Perception","slug":"senior-machine-learning-engineer-vehicle-perception-6d396019","description":"Woven by Toyota is enabling Toyota’s once-in-a-century transformation into a mobility company. Inspired by a legacy of innovating for the benefit of others, our mission is to challenge the current state of mobility through human-centric innovation — expanding what “mobility” means and how it serves society.\n\nOur work centers on four pillars: AD/ADAS, our autonomous driving and advanced driver assist technologies; Arene, our software development platform for software-defined vehicles; Woven City, a test course for mobility; and Cloud \u0026 AI, the digital infrastructure powering our collaborative foundation. Business-critical functions empower these teams to execute, and together, we’re working toward one bold goal: a world with zero accidents and enhanced well-being for all.\n\n\nTEAM\nThe Vehicle Perception team at Woven by Toyota tackles the core challenges of machine learning for 3D perception, sensor fusion, and computer vision in autonomous vehicles. Our work involves a variety of challenges, such as analyzing petabytes of multimodal driving data, solving optimization problems in computer vision, minimizing latency on hardware accelerators, deploying scalable and efficient machine learning (ML) training and evaluation pipelines, and designing novel neural network architectures to advance state-of-the-art ML for onboard perception. We are looking for doers and creative problem solvers to join us in improving mobility for everyone with human-centered automated driving solutions for personal and commercial applications.\nWHO ARE WE LOOKING FOR?\nThe team is looking for a skilled Machine Learning Engineer to help advance a cutting-edge machine learning system for perception foundation models leveraging large-scale multimodal sensor data. You will have the chance to design and implement innovative machine learning models for our next-generation autonomous vehicle platform, influencing millions of Toyota production vehicles. We are looking for individuals who are passionate about self-driving car technology and its potential impact on humanity. Furthermore, we highly value candidates who exhibit a \"giver\" mindset, consistently seeking opportunities to assist their colleagues while maintaining a strong focus on delivering solutions to production.\n","salary_min":140000,"salary_max":230000,"location":"Palo Alto, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["generative-ai","deep-learning","autonomous-vehicles","computer-vision","machine-learning"],"apply_url":"https://jobs.lever.co/woven-by-toyota/68d72c7e-e867-45e8-b81b-3315889bea75/apply","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-07T16:35:57.897Z","expires_at":"2026-11-13T17:00:18.00542Z","created_at":"2026-10-14T15:23:51.750868Z","updated_at":"2026-10-14T17:00:18.008985Z","company_name":"Woven by Toyota","company_slug":"woven-toyota","company_logo_url":"https://www.google.com/s2/favicons?domain=woven.toyota\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/5d06e771-bce8-47ec-ab25-c7a09a6ec39e"},{"id":"bb310930-8502-4a93-ad2a-1918db8f50ca","company_id":"6734f15a-40ed-4186-ae4a-d774c655ae58","title":"Principal Machine Learning Engineer, Applied AI ","slug":"principal-machine-learning-engineer-applied-ai-2ffeead3","description":"Your Impact at LILA \n We are growing our Applied AI org and seeking a Principal Machine Learning Engineer, Applied AI  with deep expertise in model post-training, evaluation, and production-oriented ML systems. You’ll shape how Lila’s AI models are adapted to customer-specific scientific needs, with a focus on turning frontier model capabilities into reliable workflows that can be evaluated, iterated and used in real customer contexts.\n Applied AI sits at the intersection of AI Research, model engineering, and product deployment. The team partners closely with AI Researchers and Software teams to adapt Lila models to customer workflows, improve model quality through post training and driving how harness engineering shapes model behavior inside the product.\n This is a high-impact senior IC role for someone who brings deep, hands-on expertise in model post-training, evaluation, and production ML systems, built over many years of solving this exact class of problem.\n What You'll Be Building \n \n Apply deep, hands-on expertise to close the last-mile gap between Lila's model capabilities and customer-specific scientific workflows.\n Lead post-training efforts using approaches such as SFT and RL (DPO, PPO/GRPO) to align model behavior with customer-specific requirements and feedback.\n Build and run evaluation loops that measure model quality, reliability, and customer fit, using patterns you've refined across many prior projects.\n Turn customer learnings, data signals, and evaluation results into concrete model improvement cycles.\n Partner with AI researchers to translate model advances into reliable, usable capabilities.\n Work with Software to integrate model behavior into end-to-end product workflows.\n Debug complex model failures using traces, evaluations, customer context, and scientific feedback.\n Mentor engineers and share reusable patterns for model adaptation, evaluation, and deployment, drawing on lessons learned across a long track record of shipping ML systems. \n \n What You'll Need to Succeed \n \n Minimum 2-3 years of hands-on post-training experience (SFT, RL methods such as DPO/PPO/GRPO) and evaluation system design, gained from having solved these problems many times before.\n Strong software engineering skills in Python and modern ML frameworks like PyTorch.\n A demonstrated ability to debug ambiguous, high-stakes model behavior quickly, using data, traces, logs, and qualitative feedback - the kind of judgment that only comes from having seen many failure modes before.\n Experience leading technical work across research and engineering teams.\n Deep familiarity with large language models, multi-modal models, or agentic AI systems.\n Clear communication skills for translating customer needs into technical approaches, and for explaining complex model behavior to both technical and non-technical audiences. \n \n Bonus Points For \n \n Experience adapting models for customer-facing or production workflows, ideally in scientific, technical, or data-intensive domains.\n Experience with RL post-training, such as RLHF, GRPO, or tool-augmented RL.\n Experience building evaluation harnesses, model monitoring, or quality dashboards.\n Experience training MoE architectures.\n A track record of mentoring engineers and being sought out as a go-to technical expert, rather than a people manager or strategy owner.\n Compensation \n We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.\n U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.\n International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.\n Expected Base Salary Range\n $252,000 — $336,000 USD \n About LILA \n Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.\n LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.\n Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an en","salary_min":252000,"salary_max":336000,"location":"Boston, MA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"principal","tags":["pytorch","llm","reinforcement-learning","mlops","agents","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/lilasciences/jobs/4400744009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-07T15:52:55Z","expires_at":"2026-11-13T17:03:03.861561Z","created_at":"2026-10-14T15:25:07.336193Z","updated_at":"2026-10-14T17:03:03.864854Z","company_name":"Lila Sciences","company_slug":"lila-sciences","company_logo_url":"https://www.google.com/s2/favicons?domain=lila.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/bb310930-8502-4a93-ad2a-1918db8f50ca"},{"id":"ec0beeba-9413-474e-88c1-ed020950d565","company_id":"6734f15a-40ed-4186-ae4a-d774c655ae58","title":"Senior / Staff Machine Learning Engineer, Applied AI","slug":"senior-staff-machine-learning-engineer-applied-ai-80cee647","description":"Your Impact at LILA \n We are growing our Applied AI org and seeking a Senior / Staff Machine Learning Engineer, Applied AI with expertise in model post-training, evaluation, and production-oriented ML systems. You’ll shape how Lila’s AI models are adapted to customer-specific scientific needs, with a focus on turning frontier model capabilities into reliable workflows that can be evaluated, iterated and used in real customer contexts.\n Applied AI sits at the intersection of AI Research, model engineering, and product deployment. The team partners closely with AI Researchers and Software teams to adapt Lila models to customer workflows, improve model quality through post training and driving how harness engineering shapes model behavior inside the product.\n This is the right role for someone who wants to go deep on model adaptation and evaluation, take real ownership of hard technical problems, and grow into broader scope over time. \n What You'll Be Building \n \n Apply hands-on expertise to close the last-mile gap between Lila's model capabilities and customer-specific scientific workflows.\n Run post-training work using approaches such as SFT and RL (DPO, PPO/GRPO) to align model behavior with customer-specific requirements and feedback.\n Run evaluation loops that measure model quality, reliability, and customer fit.\n Turn customer learnings, data signals, and evaluation results into concrete model improvement cycles.\n Partner with AI researchers to translate model advances into reliable, usable capabilities.\n Work with Software to integrate model behavior into end-to-end product workflows.\n Debug complex model failures using traces, evaluations, customer context, and scientific feedback.\n \n What You'll Need to Succeed \n \n Minimum 1-2 years of hands-on post-training experience (SFT, RL methods such as DPO/PPO/GRPO) and evaluation system design, gained from having solved these problems many times before.\n Strong software engineering skills in Python and modern ML frameworks like PyTorch.\n A demonstrated ability to debug ambiguous, high-stakes model behavior quickly, using data, traces, logs, and qualitative feedback - the kind of judgment that only comes from having seen many failure modes before.\n Deep familiarity with large language models, multi-modal models, or agentic AI systems.\n Clear communication skills for translating customer needs into technical approaches, and for explaining complex model behavior to both technical and non-technical audiences. \n \n Bonus Points For \n \n Experience adapting models for customer-facing or production workflows, ideally in scientific, technical, or data-intensive domains.\n Experience with RL post-training, such as RLHF, GRPO, or tool-augmented RL.\n Experience building evaluation harnesses, model monitoring, or quality dashboards.\n Experience training MoE architectures.\n A track record of mentoring engineers and being sought out as a go-to technical expert, rather than a people manager or strategy owner.\n Compensation \n We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.\n U.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.\n International Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.\n Expected Base Salary Range\n $180,000 — $298,000 USD \n About LILA \n Lila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.\n LILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.\n Guided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.\n We’re All In \n Lila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran s","salary_min":180000,"salary_max":298000,"location":"Boston, MA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["pytorch","mlops","agents","llm","reinforcement-learning","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/lilasciences/jobs/4400741009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-07T15:52:17Z","expires_at":"2026-11-13T17:03:04.138761Z","created_at":"2026-10-14T15:25:07.633139Z","updated_at":"2026-10-14T17:03:04.141843Z","company_name":"Lila Sciences","company_slug":"lila-sciences","company_logo_url":"https://www.google.com/s2/favicons?domain=lila.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/ec0beeba-9413-474e-88c1-ed020950d565"}],"page":1,"per_page":20,"total":606,"total_is_exact":true,"total_pages":31}