{"access":{"advertiser_pricing_url":"https://aidevboard.com/pricing","catalog_url":"https://aidevboard.com/api/v1/catalog","description":"Public read endpoints are open and free. API keys are optional for stable agent identity and keyed hourly throttling.","docs_url":"https://aidevboard.com/docs","mode":"open","register_url":"https://aidevboard.com/api/v1/register"},"degraded":false,"estimated":false,"has_next":true,"jobs":[{"id":"7466ea68-e22a-4989-93a5-1db0ae5979e1","company_id":"a0000000-0000-0000-0000-000000000003","title":"Software Engineering Manager, Public Sector ","slug":"software-engineering-manager-public-sector-dd16fefc","description":"Scale AI’s Public Sector business is growing quickly as government agencies adopt AI to support critical national security, defense, and public sector missions. We’re looking for a hands-on Engineering Manager to lead a team of software engineers building core products and infrastructure for these customers.\n This role is ideal for someone who thrives in technical environments, enjoys managing teams while staying close to the code, and wants to work on meaningful problems that impact real world operations across the U.S. government. You’ll play a critical role in delivering backend systems, distributed platforms, and ML tooling used by our public sector partners—all while helping your team grow and execute.\n You’ll split your time between technical planning and execution (50%) and people management and team development (50%) , leading a team of 6-8 engineers. You’ll work cross-functionally with product, security, and customer-facing teams to ensure our engineering efforts meet complex federal compliance, security, and performance needs.\n Must be able to commute to office three times per week \n You will: \n \n Recruit a high-performing engineering team. \n Drive engineering productivity. Provide guidance, mentorship, and technical leadership to a team of engineers working on Generative AI projects. \n Collaborating with cross-functional teams to define, design, and execute strategic roadmap.\n Navigate and deliver outcomes while navigating through complex public sector compliance requirements and frameworks.\n Design and implement scalable backend systems for Federal customers, leveraging Scale's modern and cloud-native AI infrastructure\n Develop distributed systems, data-intensive applications, and machine learning infrastructure to enable real impact for mission owners\n Build robust and reliable backend systems that can serve as standalone products, empowering customers to accelerate their own AI ambitions\n Participate actively in customer engagements, working closely with stakeholders to understand requirements and deliver innovative solutions\n Contribute to the platform roadmap and product strategy for Scale AI's Federal business, playing a key role in shaping the future direction of our offerings\n Have or ability to obtain a TS/SCI clearance \n \n Ideally you’d have: \n \n 5+ years of full-time engineering experience, post-graduation\n 2+ years of prior engineering management or equivalent experience and has managed an engineering team.\n Have extensive experience in software development\n Experience scaling products at hyper-growth startups\n Excitement to work with AI technologies and their applications for the public sector\n Extremely strong track record as an individual contributor\n Show a track record of mentoring and leading teams in successful projects\n Possess excellent communication and collaboration skills, and the ability to translate complex technical concepts to non-technical stakeholders\n \n Nice to haves: \n \n TS/SCI Clearance\n Deep technical knowledge of Software Development, willing to get deep into the weeds to solve problems alongside the team.\n Have experience with AI platforms and technologies, including generative models and LLMs.\n Have previous experience in government or government facing technology roles\n Experience with cloud-native technologies, full stack development, data engineering, and ml ops infrastructure\n Compensation packages at Scale for eligible roles include base salary, equity, and benefits. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position and may be inclusive of several career levels at Scale; it will be determined during the interview process based on work location and additional factors, including job-related skills, experience, qualifications, interview performance, and relevant education or training. Scale employees in eligible roles are also granted equity based compensation, subject to Board of Director approval. Your recruiter can share more about the specific salary range for your preferred location during the hiring process, and confirm whether the hired role will be eligible for equity grant. You'll also receive benefits including, but not limited to: comprehensive health, dental and vision coverage, retirement benefits, a learning and development stipend, and generous PTO. Additionally, this role may be eligible for additional benefits such as a commuter stipend. \n Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of San Francisco, New York is:\n $216,000 — $270,000 USD \n Please reference the job posting's subtitle for where this position will be located. For pay transparency purposes, the base salary range for this full-time position in the locations of Washington DC is:\n $194,400 — $243,000 USD \n Please reference the job posting's subtitle for wher","salary_min":162400,"salary_max":203000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["llm","distributed-systems","generative-ai","mlops"],"apply_url":"https://job-boards.greenhouse.io/scaleai/jobs/4715325005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-15T01:05:55Z","expires_at":"2026-08-14T14:01:49.051439Z","created_at":"2026-07-15T14:01:49.182522Z","updated_at":"2026-07-15T14:01:49.182522Z","company_name":"Scale AI","company_slug":"scale-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=scale.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/7466ea68-e22a-4989-93a5-1db0ae5979e1"},{"id":"021f3b70-f0d5-4666-a5e1-431d120b0e63","company_id":"31ae48bc-c938-4c26-a348-0bf3c089a446","title":"Senior Software Engineer - GPU Kernel Authoring \u0026 Optimization","slug":"senior-software-engineer-gpu-kernel-authoring-optimization-d4eed12b","description":"CoreWeave is The Essential Cloud for AI™. Built for pioneers by pioneers, CoreWeave delivers a platform of technology, tools, and teams that enables innovators to build and scale AI with confidence. Trusted by leading AI labs, startups, and global enterprises, CoreWeave combines superior infrastructure performance with deep technical expertise to accelerate breakthroughs and turn compute into capability. Founded in 2017, CoreWeave became a publicly traded company (Nasdaq: CRWV) in March 2025. Learn more at  www.coreweave.com . \n About the role: \n CoreWeave is the top-rated AI-cloud for high-performance GPU infrastructure across AI/ML, visual effects, rendering, and real-time inference. Our stack is engineered for speed, scale, and cost-efficiency—an unmatched alternative to traditional hyperscalers. At CoreWeave, infrastructure is the product.\n We're looking for a Senior Engineer for CoreWeave's Benchmarking \u0026 Performance team, focused on kernel authoring and optimization. You will write, profile, and tune the GPU kernels that sit on the critical path of large-scale model serving—squeezing maximum throughput and minimum latency out of every SM, tensor core, and byte of memory bandwidth. You will also aid us in achieving industry-leading end-to-end performance benchmarking publications such as MLPerf.\n You will be an owner who leads designs, raises engineering standards, and delivers measurable improvements to latency, throughput, and reliability across our inference stack. You'll partner with product, orchestration, and hardware teams to turn kernel-level wins into end-to-end gains and meet strict P99 SLAs at scale.\n \n Author, profile, and optimize CUDA kernels—GEMMs, attention, MoE routing, quantization, KV-cache, and fused epilogues—on the critical path of LLM inference.\n Optimize for the hardware: exploit tensor cores and tune occupancy, memory coalescing, shared-memory/register usage, and overlap of compute with data movement.\n Use kernel-authoring DSLs and compilers to prototype and ship kernels quickly without sacrificing performance.\n Benchmark rigorously: build reproducible microbenchmarks and roofline analyses, and validate that kernel-level wins translate to end-to-end latency/throughput gains across model-serving stacks (vLLM, TensorRT-LLM, llm-d, SGLang).\n Implement and maintain benchmarking workflows for end-to-end MLPerf Inference (and Training) runs, including workload setup, cluster configuration, runbooks, and result validation.\n Lead design reviews and drive architecture within the team; decompose multi-service work into clear milestones.\n Mentor junior engineers; review cross-team designs and elevate coding/testing standards.\n Help ensure reproducible, well-documented benchmarking and kernel-optimization processes.\n \n Who You Are: \n \n 5+ years of experience building high-performance computing, GPU/accelerator software, or performance-critical systems.\n Hands-on CUDA experience is required—you have written and optimized custom kernels and are fluent with the CUDA programming and memory model.\n Deep understanding of GPU architecture and performance: tensor cores, warp/occupancy tuning, the memory hierarchy and bandwidth, NVLink/PCIe, and profiling with Nsight Compute/Systems.\n Strong coding in C++ and Python; comfortable reading and writing low-level, performance-sensitive code.\n Familiarity with model-serving stacks (vLLM, TensorRT-LLM, llm-d, SGLang) and the kernels that dominate their inference cost.\n Strong communicator comfortable collaborating with cross-functional teams and external partners.\n \n Preferred: \n \n Triton or Mojo for authoring custom GPU kernels — highly desired.\n CuTe DSL for Python-based kernel authoring on NVIDIA GPUs.\n JAX and its Pallas kernel language for authoring kernels on GPU/TPU.\n HIP / ROCm and AMD GPU experience.\n NCCL and collective-communication performance.\n Experience with alternative accelerators such as Google TPUs and Meta's MTIA.\n Familiarity with kernel-authoring DSLs and nano-compilers such as KNYFE and its Block DSL.\n Experience with Kubernetes at production scale.\n Experience with SUNK (Slurm on Kubernetes) / Slurm for scheduling large GPU jobs.\n Experience running MLPerf submissions or similar large-scale audited benchmarks.\n Contributions to OSS projects such as vLLM, SGLang, PyTorch, Triton, or CUTLASS.\n \n Wondering if you're a good fit? \n We believe in investing in our people, and value candidates who can bring their own diversified experiences to our teams – even if you aren't a 100% skill or experience match.\n Why CoreWeave? \n Help shape an industry-defining inference platform that enables teams to deploy generative AI and real-time applications at scale. If squeezing every last microsecond out of GPU kernels and delivering reliable model serving excites you, this is the place to build. We're in an exciting stage of hyper-growth that you will not want to miss out on. We're not afraid of a little chaos, and we're constantly ","salary_min":182000,"salary_max":242000,"location":"Sunnyvale, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["mlops","generative-ai","llm","pytorch","computer-graphics","gpu","jax"],"apply_url":"https://coreweave.com/careers/job?4697100006\u0026board=coreweave\u0026gh_jid=4697100006","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T22:01:55Z","expires_at":"2026-08-14T14:06:51.780451Z","created_at":"2026-07-15T14:06:51.909822Z","updated_at":"2026-07-15T14:06:51.909822Z","company_name":"CoreWeave","company_slug":"coreweave","company_logo_url":"https://www.google.com/s2/favicons?domain=coreweave.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/021f3b70-f0d5-4666-a5e1-431d120b0e63"},{"id":"96c4b57f-c214-4de0-829c-cda4957c7a17","company_id":"a0000000-0000-0000-0000-000000000003","title":"Senior Software Engineer, Agent Oversight","slug":"senior-software-engineer-agent-oversight-a8682235","description":"About Scale\n Scale’s mission is to develop reliable AI systems for the world’s most important decisions. As the leading AI data foundry, we provide the high-quality data and full-stack technologies that power the world’s most advanced models — fueling breakthroughs in generative AI, defense, and autonomous vehicles. We partner with leading enterprises and governments to bring AI into production that performs when it matters most, combining rigorous evaluation with full-stack deployment so our customers can build AI they can trust.\n About the Team\n Applied Intelligence Systems team is part of the Scale Generative AI Platform (SGP), focused on pushing the frontier of what agentic applications can do across diverse enterprise and government use cases. We build the infrastructure and tooling that power Agentic AI in production, paired with applied ML research, design, and evaluation to ensure these systems perform reliably at the scale our customers demand. We’re growing fast, with increasing traction across both commercial and public sector customers, and we’re just getting started — this team will define what dependable, production-grade agentic AI looks like.\n About the Role\n As a Software Engineer on Agent Oversight, you will build the platform infrastructure that lets our production agents be observed, evaluated, and improved at scale. This includes building observability tooling, evaluation harnesses, and the pipelines that connect them to improvement loops. Whether building foundational infrastructure or partnering closely with ML engineers on production workflows, you will own your systems end-to-end while maintaining rigorous technical standards.\n You will:\n \n Design and build core platform capabilities for deploying, monitoring, and evaluating agentic applications in production\n Build reliable APIs and data pipelines that capture agent telemetry, evaluation signals, and performance metrics at scale\n Work alongside ML engineers where platform work intersects with evaluation or improvement systems — bringing enough ML fluency to reason about model behavior, evaluation quality, and improvement loops while owning the software systems that make those workflows reliable\n Own the reliability, scalability, and observability of platform components serving multiple concurrent enterprise and government customers\n Work cross-functionally with product, forward deployed engineering, and customers to translate real-world deployment requirements into platform features\n Build features end-to-end: system design, implementation, debugging, and testing\n Participate in high-velocity experimentation to validate platform capabilities against real customer usage\n \n Requirements:\n \n 4+ years of professional software engineering experience, with strong fundamentals in backend/distributed systems, APIs, and data pipeline design\n Hands-on experience building production software for ML/LLM-powered products or platforms, such as evaluation systems, observability/monitoring, experimentation infrastructure, agent runtimes, model-serving-adjacent services, or telemetry/data pipelines\n Working knowledge of how LLM or ML systems behave in production: evaluation signals, failure modes, prompt/tool-calling workflows, experiment results, data quality issues, and the tradeoffs between offline evals and live customer behavior\n Experience partnering closely with ML engineers or applied researchers to turn prototypes, eval loops, or model-improvement workflows into reliable platform capabilities, without needing to own model training, modeling strategy, or research direction\n Experience building infrastructure or platforms that other engineering teams build on top of (internal platform, developer tools, or similar)\n Track record of taking ownership of features or components end-to-end — from design through production — within a larger platform or system\n Comfortable operating in an ambiguous, fast-changing domain where tooling and best practices are still being defined\n Strong problem-solving skills and the ability to work independently or as part of a tight-knit, cross-functional team\n Excited to work directly with ML engineers and customer-facing teams, including challenging assumptions in designs and metrics when platform behavior, model behavior, and customer needs intersect\n Gives direct, substantive feedback on designs and code, and takes it the same way — and mentors others as they grow\n \n Nice to have:\n \n Deep experience building or maintaining observability, monitoring, or evaluation systems for ML/LLM-powered products in production\n Familiarity with agent architectures — tool use, planning, multi-agent orchestration\n Exposure to MLOps, feature stores, model serving, or experiment infrastructure\n Experience working in regulated or enterprise contexts\n Experience reviewing others’ technical designs or mentoring engineers at a senior/staff level\n Compensation packages at Scale for eligible roles include base s","salary_min":216000,"salary_max":270000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["data-pipeline","mlops","generative-ai","agents","autonomous-vehicles","llm","distributed-systems"],"apply_url":"https://job-boards.greenhouse.io/scaleai/jobs/4714509005","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T20:12:46Z","expires_at":"2026-08-14T14:01:47.306812Z","created_at":"2026-07-15T14:01:47.543291Z","updated_at":"2026-07-15T14:01:47.543291Z","company_name":"Scale AI","company_slug":"scale-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=scale.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/96c4b57f-c214-4de0-829c-cda4957c7a17"},{"id":"f115ce97-f6c4-4c2d-9602-6a9e48528e12","company_id":"b6db41bc-ba14-4906-b2f7-a3ce9289a346","title":"Software Engineer, AI Platform","slug":"software-engineer-ai-platform-305af12e","description":"WHO WE ARE\n\nNotion is the collaborative AI workspace where teams and agents think together https://www.youtube.com/watch?v=vkpYpWfEK5s. We're building one place where your knowledge, projects, meetings, and AI tools live side by side, so work is faster, clearer, and less fragmented. Millions of individuals, small teams, and large companies run their work on Notion.\n\n\n\nNotinos (our employees) are customer zero in bringing this future of work to life. We care about craft, building things that last, and the belief that great work is still fundamentally human. Our goal isn’t to ship the next feature. Each and every team of Notinos is working to set the standard for how humans work together in the AI era. From building a business’s system of record to making and managing AI agents to automating away the busy work, we care deeply about giving our customers more time for their life’s work.\n\n\n\n\nABOUT THE ROLE:\n\nMillions of people use Notion — and this number is increasing every day. That means millions of people trust us to deliver a fast, reliable, and secure experience, and we value this more than anything. We want to keep earning trust, while also continuing to amaze our users with the tools they can build in Notion.\n\nThe AI Platform team is responsible for building the shared foundations that let Notion ship AI products quickly and operate them safely at scale. You’ll join a team of talented engineers focused on making speed and quality compatible: reliability and availability through provider changes, quality and correctness systems like evals and release gates, observability that makes failures explainable, and shared primitives for model integrations, context management, long-running actions, and cost/performance tradeoffs. Notion’s AI platform is vital to helping product teams move faster with production-grade guardrails as models, providers, and AI capabilities rapidly evolve.\n\nThis role can be based in either San Francisco or New York City. We work from our offices on Mondays, Tuesdays and Thursdays (our Anchor Days) because we do our best thinking and building together in person. We’re looking for someone who’s excited to work alongside the team during those days.\n\n\n\n\nWHAT YOU'LL ACHIEVE:\n\n - You'll own and play a pivotal role in the prototyping, development and scaling of systems and core AI platform primitives.\n\n - You’ll partner closely with product teams to provide paved paths and production-ready guardrails that help new AI features ship faster with less duplicated work.\n\n - You’ll work across infrastructure, shared libraries, APIs, and product integration points to make AI platform capabilities easy to adopt and high-leverage across Notion.\n\n - You’ll operate critical AI systems in production, using observability and diagnostics to understand provider/model behavior, debug failures, improve latency and cost, and evolve systems with minimal user disruption.\n\n - You’ll help Notion safely adopt new models, providers, and AI capabilities through versioning, controlled rollouts, compatibility layers, and clear quality/reliability gates.\n\n\n\n\nSKILLS YOU'LL NEED TO BRING:\n\n - Passion for AI systems at scale: You’ve worked on LLM, ML platform, data, or infrastructure teams that own critical shared systems. You understand the challenges of scaling reliability, latency, cost, and quality as usage and model complexity grow. You care deeply about building platforms that are dependable, efficient, and easy for other engineers to use.\n\n - Adaptable and curious: You like going deep on how systems behave in practice, especially when models, providers, and product requirements are changing quickly. You’re eager to use AI tools to work smarter and are willing to move across backend, infrastructure, libraries, and product code when that’s what the problem requires.\n\n - Extreme ownership: You’re comfortable working across ambiguous problem spaces, aligning stakeholders around a clear path forward, and driving execution with accountability. You take ownership of platform outcomes including reliability, quality, adoption, and operational follow-through beyond team boundaries.\n\n - Thoughtful problem-solving: For you, problem-solving starts with a clear and accurate understanding of the context. You can decompose ambiguous system behavior, debug across layers, and work toward clean, pragmatic solutions by yourself or with teammates. You’re comfortable asking for help when you get stuck.\n\n - Pragmatic and business-oriented: You understand that AI platform work is full of tradeoffs across quality, latency, cost, reliability, and speed of execution. You prioritize based on product and business impact, balancing craft with urgency and operational simplicity.\n\n\n\n\nNICE TO HAVES:\n\n - 2-4 years of experience as a Software Engineer\n\n - Experience with applied AI product development (prompting, evals, model integrations, or quality measurement).\n\n - You've built out and scaled data processing pipeli","salary_min":180000,"salary_max":201000,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"junior","tags":["agents","mlops","llm","data-pipeline","platform"],"apply_url":"https://jobs.ashbyhq.com/notion/a9d4a192-d31c-48d2-8156-e2a75d98eec1/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-14T14:23:31.706Z","expires_at":"2026-08-14T14:04:38.933673Z","created_at":"2026-07-15T14:04:39.064153Z","updated_at":"2026-07-15T14:04:39.064153Z","company_name":"Notion","company_slug":"notion","company_logo_url":"https://www.google.com/s2/favicons?domain=notion.so\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f115ce97-f6c4-4c2d-9602-6a9e48528e12"},{"id":"5e3167da-1058-431f-8718-8bb9f1e4656f","company_id":"3d233526-89a8-48ea-b0ed-3304a35b8acf","title":"Software Engineer II, ML Ops","slug":"software-engineer-ii-ml-ops-461a0523","description":"At WHOOP, we're on a mission to unlock human performance. WHOOP empowers members to perform at a higher level through a deeper understanding of their bodies and daily lives.\nWe are looking for a talented and passionate Software Engineer II to join our MLOps team, focusing on the development and optimization of ML cloud infrastructure. In this role, you will play a critical part in supporting our Data Science and AI teams by building robust, scalable systems for the productionalization of machine learning models. Your work will be at the heart of bringing advanced ML/AI solutions into production, ensuring they are reliable, scalable, and ready to drive value across WHOOP.\n","salary_min":125000,"salary_max":175000,"location":"Boston, MA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"mid","tags":["cloud","mlops","machine-learning","research"],"apply_url":"https://jobs.lever.co/whoop/82635467-6cfb-4e8b-967e-73355a0d0b8f/apply","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-13T16:24:21.247Z","expires_at":"2026-08-14T14:19:08.279053Z","created_at":"2026-07-15T14:19:08.388557Z","updated_at":"2026-07-15T14:19:08.388557Z","company_name":"WHOOP","company_slug":"whoop","company_logo_url":"https://www.google.com/s2/favicons?domain=whoop.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/5e3167da-1058-431f-8718-8bb9f1e4656f"},{"id":"cb44c455-97e8-4e00-ab4f-3fab00fa325f","company_id":"72014eb6-e84d-48c2-af5c-5424ebec0b3c","title":"Staff Machine Learning Engineer","slug":"staff-machine-learning-engineer-586d7131","description":"Reddit is a community of communities. It’s built on shared interests, passion, and trust, and is home to the most open and authentic conversations on the internet. Every day, Reddit users submit, vote, and comment on the topics they care most about. With 100,000+ active communities and approximately 126 million daily active unique visitors, Reddit is one of the internet’s largest sources of information. For more information, visit www.redditinc.com .\n Job Duties: Design, develop, and train advanced machine learning models, including deep neural networks, transformer-based architectures, and reinforcement learning systems, to power large-scale online advertising ranking and optimization platforms. Lead the development and optimization of complex feature representations, including high-dimensional embeddings, contextual and temporal signals, and cross-session user behavior modeling. Drive end-to-end model lifecycle execution, including system architecture design, large-scale experimentation, model deployment, performance monitoring, and iterative infrastructure improvements in production environments. Collaborate closely with product, data, and infrastructure engineering teams to translate business objectives into scalable, statistically rigorous modeling solutions. Conduct advanced experiment design and causal analysis to evaluate model impact and inform strategic decisions. Provide technical leadership and mentorship to machine learning engineers and contribute to organization-wide modeling standards, best practices, and long-term technical strategy. Shape the long-term modeling vision across multiple advertising domains, including conversion optimization, application advertising, shopping, and brand advertising. Full-time telecommuting is an option. \n Requirements: Master’s degree in Computer Science, Engineering (any field) or related quantitative discipline and (3) three years of experience in the job offered or related occupation. \n Special Skill Requirements: 1) Python, Java, and Scala; 2) C++, Go, or Rust; 3) major machine learning frameworks and libraries; 4) applied statistics, hypothesis testing and experiment design for online machine learning systems; 5) large-scale data processing and analytics frameworks; 6) deployment and operation of production systems in containerized and distributed environments; 7) Designing and training advanced models, including deep neural networks, transformer-based architectures, and reinforcement learning models; 8) marketplace dynamics, such as real-time bidding (RTB) or pacing control systems; 9) developing and optimizing online advertising systems, including ad ranking, targeting, and market place; 10) providing technical leadership, mentorship, or guidance to other machine learning engineers. Any suitable combination of education, training and/or experience is acceptable. Full-time telecommuting is an option. \n Benefits: \n \n Comprehensive Healthcare Benefits and Income Replacement Programs\n 401k with Employer Match\n Global Benefit programs that fit your lifestyle, from workspace to professional development to caregiving support\n Family Planning Support\n Gender-Affirming Care\n Mental Health \u0026 Coaching Benefits\n Flexible Vacation \u0026 Paid Volunteer Time Off\n Generous Paid Parental Leave \n \n Submit a resume with references using the apply button on this posting or by email at:  applicationsreview@reddit.com at Req.# 1016.83.2.\n  \n Pay Transparency: \n This job posting may span more than one career level.\n In addition to base salary, this job is eligible to receive equity in the form of restricted stock units, and depending on the position offered, it may also be eligible to receive a commission. Additionally, Reddit offers a wide range of benefits to U.S.-based employees, including medical, dental, and vision insurance, 401(k) program with employer match, generous time off for vacation, and parental leave. To learn more, please visit  https://www.redditinc.com/careers/ .\n To provide greater transparency to candidates, we share base pay ranges for all US-based job postings regardless of state. We set standard base pay ranges for all roles based on function, level, and country location, benchmarked against similar stage growth companies. \n The base pay range for this position is: $230,000.00 - $322,000.00 USD\n  \n #LI-DNI\n In select roles and locations, the interviews will be recorded, transcribed and summarized by artificial intelligence (AI). You will have the opportunity to opt out of recording, transcription and summarization prior to any scheduled interviews.\n During the interview, we will collect the following categories of personal information: Identifiers, Professional and Employment-Related Information, Sensory Information (audio/video recording), and any other categories of personal information you choose to share with us. We will use this information to evaluate your application for employment or an independent contractor role, as applicable.  We ","salary_min":230000,"salary_max":322000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["healthcare","mlops","reinforcement-learning","deep-learning","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/reddit/jobs/8054426","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-13T13:51:13Z","expires_at":"2026-08-14T14:10:39.416828Z","created_at":"2026-07-15T14:10:39.537452Z","updated_at":"2026-07-15T14:10:39.537452Z","company_name":"Reddit","company_slug":"reddit","company_logo_url":"https://www.google.com/s2/favicons?domain=www.reddit.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/cb44c455-97e8-4e00-ab4f-3fab00fa325f"},{"id":"da65e8fc-123b-47b4-a19f-f1b5fde0fc84","company_id":"77beb456-fc80-40a4-b773-f0b17d1ece4c","title":"AI Infrastructure Engineer","slug":"ai-infrastructure-engineer-aabaa04d","description":"ABOUT MESHY\n\nHeadquartered in Silicon Valley, Meshy is the leading 3D generative AI company on a mission to Unleash 3D Creativity by transforming the content creation pipeline. Meshy makes it effortless for both professional artists and hobbyists to create unique 3D assets—turning text and images into stunning 3D models in just minutes. What once took weeks and cost $1,000 now takes just 2 minutes and $1.\n\nOur world-class team of top experts in computer graphics, AI, and art includes alumni from MIT, Stanford, and Berkeley, as well as veterans from Nvidia and Microsoft. Our talent spans the globe, with team members distributed across North America, Asia, and Oceania, fostering a diverse and innovative multi-regional culture focused on solving global 3D challenges. Meshy is trusted by top developers, backed by premiere venture capital firms like Sequoia and GGV, and has successfully raised $52 Million in funding.\n\nMeshy is the market leader, recognized as the No.1 in popularity among 3D AI tools (according to 2024 A16Z Games) and No.1 in website traffic (according to SimilarWeb, with 3 Million monthly visits). The platform boasts over 5 Million users and has generated 40 Million models.\n\nFounder and CEO Yuanming (Ethan) Hu earned his Ph.D. in graphics and AI from MIT, where he developed the acclaimed Taichi GPU programming language (27K stars on GitHub, used by 300+ institutes). His work is highly influential, including an honorable mention for the SIGGRAPH 2022 Outstanding Doctoral Dissertation Award and over 2,700 research citations.\n\n\n\n\n\nABOUT THE ROLE\n\n - This role sits at the intersection of platform engineering, site reliability, and applied ML systems. The function owns the reliability, scalability, and operability of Meshy's AI model serving stack, along with core engineering infrastructure. The team operates a conventional production infrastructure (CI/CD, build systems, deployment, runtime environments) and develops a model-serving platform that connects the models developed by our Research Team to product-facing backend systems. The position is systems-heavy, production-oriented, and focused on turning experimental model artifacts into robust, observable, and cost-efficient services.\n\n\n\n\n\nJOB RESPONSIBILITIES\n\n - Responsible for the design, development, and optimization of core capabilities for the AI inference platform, including key modules such as inference services, task scheduling, service orchestration, elastic scaling, and release governance.\n\n - Participate in the development of CPU/GPU resource management systems to optimize stability, resource utilization, and cost efficiency in scenarios where online inference and training tasks are run on the same cluster.\n\n - Drive the unified management and scheduling of GPU resources, and explore the practical implementation of capabilities such as MIG, MPS, time-sharing, and virtualization in real-world business operations.\n\n - Continuously optimize the throughput, latency, and availability of the inference pipeline, refining engineering quality in complex inference pipelines, multi-model collaboration, and high-concurrency scenarios.\n\n - Focus on R\u0026D efficiency, resource and cost management, online stability, and disaster recovery architecture design to drive the company’s continuous evolution in performance, reliability, and maintainability.\n\n - Explore AI-native infrastructure and automated operations to make infrastructure smarter and more user-friendly, supporting the company’s rapid expansion during its startup phase.\n\n \n\n\nQUALIFICATIONS\n\n - Bachelor’s degree or higher; majors in Computer Science, Software Engineering, Artificial Intelligence, Telecommunications, or related fields are preferred.\n\n - 1 to 3 years of experience in backend development, infrastructure, cloud-native platforms, machine learning platforms, or AI platforms.\n\n - Proficiency in at least one of Go or Python, with solid software engineering skills and a strong commitment to code quality.\n\n - Understanding of fundamental principles in Linux, operating systems, computer networks, and distributed systems; ability to independently identify and resolve complex engineering issues.\n\n - Practical development experience with Kubernetes, Docker, microservices, or distributed systems, with a basic understanding of production system stability.\n\n - Real-world project experience in areas such as model inference, task orchestration, resource scheduling, and service stability—beyond mere conceptual understanding.\n\n - Self-motivated, curious, and a fast learner; willing to take on greater ownership and broader responsibilities in a startup environment, while continuously learning and quickly adopting new technologies.\n\n\nNICE TO HAVE\n\n - Experience with GPU inference platforms, Kubernetes schedulers, Device Plugins, or related platform development.\n\n - Familiarity with frameworks such as Ray and Ray Serve, or experience in developing and optimizing model serving, distributed in","salary_min":175000,"salary_max":300000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["generative-ai","agents","microservices","mlops","distributed-systems","infrastructure"],"apply_url":"https://jobs.ashbyhq.com/meshy/e82eca7a-4704-4af3-a84f-94c6fb5e1034/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-10T21:33:17.539Z","expires_at":"2026-08-14T14:12:17.728298Z","created_at":"2026-04-13T15:01:38.817296Z","updated_at":"2026-07-15T14:12:17.854855Z","company_name":"Meshy","company_slug":"meshy","company_logo_url":"https://www.google.com/s2/favicons?domain=meshy.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/da65e8fc-123b-47b4-a19f-f1b5fde0fc84"},{"id":"78bb5e81-4dfe-4ed2-af4d-f819687a5629","company_id":"e455f75a-a424-4955-9844-afebe8ea6eb4","title":"Cloud Infrastructure Architect, Okta Federal","slug":"cloud-infrastructure-architect-okta-federal-be26a5b7","description":"Secure Every Identity, from AI to Human Identity is the key to unlocking the potential of AI. Okta secures AI by building the trusted, neutral infrastructure that enables organizations to safely embrace this new era. This work requires a relentless drive to solve complex challenges with real-world stakes. We are looking for builders and owners who operate with speed and urgency and execute with excellence. This is an opportunity to do career-defining work. We're all in on this mission. If you are too, let's talk.\n \n  Technology, Data, and Insights (TDI) is on a mission to accelerate Okta's scale and growth. We bring world-class business acumen and technology expertise to every interaction. We also drive cross-functional collaboration and are focused on delivering measurable business outcomes.\n The TDI Infrastructure Engineering team owns the foundational platforms that power Okta's business — from cloud infrastructure and AI platform delivery to network engineering, developer productivity, observability, and client platforms. We are a team of builders who design and operate at scale, and we are in the middle of a strategic transformation: evolving our cloud practice from a self-service model into a managed, opinionated platform that the entire business can rely on.\n The Cloud Platform Architect Opportunity\n Okta Federal, Inc. is looking for a dedicated Cloud Platform Architect for TDI Infrastructure Engineering — the technical authority for how we design, build, and evolve the cloud infrastructure that underpins our AI platform and the broader workloads running across the business. You will define the architectural standards, patterns, and strategies that the Cloud Platform Engineering team builds to, and you will serve as a key partner to AI, security, and productivity architects as we scale Okta's cloud capabilities to meet increasing business demand.\n This is a hands-on builder role. We are not looking for someone who advises from a distance — we need someone who has shipped cloud infrastructure at scale and brings the credibility and depth to make sound architectural decisions in a fast-moving environment. You will operate at a critical moment: Okta's AI platform is scaling rapidly, our cloud platform team is transforming, and the foundational decisions made now will define the trajectory of our infrastructure for years.\n This role reports directly to the Director of Infrastructure Engineering.\n What You'll Be Doing\n \n Define and own Okta's Cloud Platform architecture — establish reference architectures, design standards, and guardrails that bring consistency, security, and reliability to workloads running across the business\n Lead the architecture for Kubernetes and EKS — design and evolve our cluster strategy, multi-tenancy model, networking topology, and security posture as the platform scales to support AI agent workloads and diverse business unit deployments\n Elevate Okta's AI platform — partner with AI architects and platform engineers to evolve our agent and model-serving infrastructure from its current state to a production-grade, scalable platform capable of supporting broad business adoption\n Drive multi-cloud strategy — build the evaluation framework and decision criteria for when and how Okta leverages AWS, Azure, and Google Cloud; ensure workload placement is intentional and optimized for performance, cost, and capability\n Serve as the technical anchor for the Cloud Platform Engineering team — raise the architectural quality of everything the team designs and builds as we complete the transformation from account vending to a managed platform model\n Partner cross-functionally with AI, security, and productivity architects, product managers, and business unit stakeholders to ensure cloud infrastructure decisions align with Okta's product, compliance, and operational requirements — including support for federal programs and FedRAMP environments\n Partner cross-functionally to design cloud-native solutions that can be effectively adapted for air-gapped, self-hosted environments like US Secret (SIPRNet) and US Top Secret (JWICS).\n Help architect and validate foundational Kubernetes and infrastructure designs within unclassified AWS GovCloud sandboxes. You will ensure these commercial-side designs translate seamlessly when tested against emulators that simulate the strict constraints of air-gapped networks.\n Ensure our commercial cloud platform architecture shares foundational DNA with our highly regulated deployments, aligning with DoD-centric frameworks like the USAF's \"Big Bang\" architecture and utilizing Iron Bank hardened containers where applicable.\n \n What You'll Bring to the Role\n \n 10+ years of hands-on cloud infrastructure experience with deep, demonstrated expertise in one or more major cloud providers (AWS, GCP, or Azure) — including compute, networking, storage, IAM, and managed services at enterprise scale; AWS experience is preferred given our current environmen","salary_min":244000,"salary_max":336000,"location":"Washington, DC","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["fine-tuning","mlops","cloud","agents","data-pipeline","embeddings","infrastructure"],"apply_url":"https://www.okta.com/company/careers/opportunity/8004104?gh_jid=8004104","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-09T17:52:28Z","expires_at":"2026-08-14T14:11:18.197322Z","created_at":"2026-07-10T14:08:46.130561Z","updated_at":"2026-07-15T14:11:18.324586Z","company_name":"Okta","company_slug":"okta","company_logo_url":"https://www.google.com/s2/favicons?domain=okta.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/78bb5e81-4dfe-4ed2-af4d-f819687a5629"},{"id":"4d6c08b7-a823-4c23-8acb-143bb6fe1561","company_id":"19a78c6a-11dc-4d21-8273-0d2d2bad39b1","title":"Staff Machine Learning Engineer","slug":"staff-machine-learning-engineer-3e90db0f","description":"Toast creates technology to help restaurants and local businesses succeed in a digital world, helping business owners operate, increase sales, engage customers, and keep employees happy.\n The Machine Learning Platform team builds and operates the core infrastructure that powers ML across Toast — the feature store, model hosting and serving, the experimentation platform, training pipelines, and the tooling ML engineers and data scientists rely on every day. Our work directly enables the models that drive personalization, forecasting, fraud detection, search, and the growing set of AI-powered experiences shipping to restaurants.\n Toast is seeking a Staff Software Engineer to act as a technical leader on the ML Platform team, shaping the systems that will carry Toast's ML capabilities into the next decade. The role involves driving architectural direction across the platform, delivering foundational infrastructure that other teams build on, and elevating fellow engineers. The ideal candidate is a domain expert who partners with ML engineers, data scientists, product, and infrastructure leadership on high-leverage opportunities.\n This position suits an engineer comfortable writing production code, leading technical design for distributed systems, and influencing organizational decisions about how Toast builds and deploys ML.\n A day in the life (Responsibilities) \n \n Own technical direction of the ML Platform — feature store, model hosting and serving, experimentation, training infrastructure — driving architectural decisions around scalability, reliability, latency, and cost\n Lead design and delivery of large-scope platform initiatives from conception through production, coordinating across ML, data, and infrastructure teams\n Identify and resolve systemic technical challenges: online/offline feature parity, model deployment friction, experimentation velocity, GPU utilization, cross-team dependencies\n Set and maintain a high engineering quality bar through hands-on code contributions, design reviews, and mentorship of platform and ML-adjacent engineers\n Partner with ML engineering, data science, product, and platform leadership to translate ML strategy into technical roadmaps\n Define the paved paths ML teams use to ship models safely — from feature registration through canary rollout, monitoring, and rollback\n Leverage AI-augmented development tools to increase development velocity and code quality\n \n What you'll need to thrive (Requirements): \n \n 8+ years delivering complex backend or infrastructure systems at scale\n Direct experience building or operating core ML infrastructure — feature stores, model serving, experimentation platforms, training orchestration, or equivalent\n Mastery of a modern backend language such as Python, Java, Kotlin, Go, or Scala\n Deep proficiency with distributed systems concepts: consistency, latency, throughput, fault tolerance, and observability\n Strong understanding of data modeling, query languages, and the online/offline data patterns that underpin ML systems\n Demonstrated technical leadership, with ability to drive cross-team alignment and influence engineering, product, and business stakeholders\n Bachelor's degree in Computer Science or a related field, or equivalent practical experience\n \n Nice to Haves: \n \n Hands-on experience with open-source or commercial ML platform components (e.g. Tecton, MLflow, SageMaker, Databricks)\n Experience building or operating experimentation / A-B testing platforms at scale\n Familiarity with real-time streaming systems (Kafka, Flink, Spark Streaming) and their use in feature computation\n Experience serving LLMs or large deep-learning models in production, including GPU capacity planning and inference optimization\n Comfort with Kubernetes and modern cloud-native infrastructure\n Prior work supporting internal-developer-facing platforms with a product mindset\n \n AI at Toast \n At Toast, one of our company values is that we're hungry to build and learn. We believe learning new AI tools empowers us to build for our customers faster, more independently, and with higher quality. We provide these tools across all disciplines, from Engineering and Product to Sales and Support, and are inspired by how our Toasters are already driving real value with them. The people who thrive here are those who embrace changes that let us build more for our customers; it’s a core part of our culture.\n Our Total Rewards Philosophy  We strive to provide competitive compensation and benefits programs that help to attract, retain, and motivate the best and brightest people in our industry. Our total rewards package goes beyond great earnings potential and provides the means to a healthy lifestyle with the flexibility to meet Toasters’ changing needs. Learn more about our benefits at  https://careers.toasttab.com/toast-benefits .\n #LI-REMOTE\n The base salary range for this role is listed below. The starting salary will be determined based on skills, experience","salary_min":151000,"salary_max":242000,"location":"Remote (US)","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["distributed-systems","mlops","llm","machine-learning"],"apply_url":"https://careers.toasttab.com/jobs?gh_jid=8031086","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-09T14:54:23Z","expires_at":"2026-08-14T14:11:50.650642Z","created_at":"2026-07-10T14:09:17.56432Z","updated_at":"2026-07-15T14:11:50.778819Z","company_name":"Toast","company_slug":"toast","company_logo_url":"https://www.google.com/s2/favicons?domain=pos.toasttab.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/4d6c08b7-a823-4c23-8acb-143bb6fe1561"},{"id":"badeb1d6-7048-4b26-8dc7-7015a80bf56b","company_id":"d66267b6-f404-410f-9b8e-fe8bbcfcaf1b","title":"Senior Software Engineer - Python and Data Ecosystem","slug":"senior-software-engineer-python-and-data-ecosystem-9f871560","description":"About ClickHouse\n Recognized on the 2025 Forbes Cloud 100 list, ClickHouse is one of the most innovative and fast-growing private cloud companies. With more than 3,000 customers and ARR that has grown over 250 percent year over year, ClickHouse leads the market in real-time analytics, data warehousing, observability, and AI workloads.\n The company’s sustained, accelerating momentum was recently validated by a $400M Series D financing round. Over the past three months, customers including Capital One, Lovable, Decagon, Polymarket, and Airwallex have adopted the platform or expanded existing deployments. These customers join an established base of AI innovators and global brands such as Meta, Cursor, Sony, and Tesla.\n We’re on a mission to transform how companies use data. Come be a part of our journey!\n The Connectors team is the bridge between ClickHouse and the broader data ecosystem. We build and maintain the integrations that make ClickHouse accessible to millions of developers, data practitioners, and AI agents worldwide from high-level data visualization plugins (Tableau, PowerBI, Superset, Metabase) to connectors for data frameworks (Apache Spark, Flink, Kafka Connect, Fivetran), orchestration platforms, and AI tooling.\n Our work directly shapes how companies process massive datasets: real-time analytics platforms ingesting millions of events per second, observability systems monitoring global infrastructure, and increasingly, the AI-powered data applications redefining how teams work with data. We collaborate closely with the open-source community, internal teams, and enterprise users to ensure ClickHouse integrations set the standard for performance, reliability, and developer experience.\n About the role\n As a Senior Software Engineer specializing in Python and the Data Ecosystem , you'll be a core contributor owning and evolving critical parts of ClickHouse's data engineering ecosystem. This role sits at the intersection of high-performance database engineering and developer experience. You'll craft tools that enable Data Engineers and Data Scientists to harness ClickHouse's speed and scale in the frameworks they already use.\n We're looking for someone who has lived the Data Engineer or Data Scientist experience firsthand. The data practitioner's world is shifting rapidly: databases are no longer just query targets, but they're becoming active participants in AI-powered workflows, serving as vector stores for RAG pipelines, backends for LLM-powered agents, and real-time feature stores for ML inference. You understand these workflows not from the outside, but because you've operated within them. You don't just build integrations, you bring product-level insight into what we should build and why.\n You'll own the full lifecycle of key Python integrations, driving architecture, performance, and feature direction across:\n \n Orchestration Platforms: Apache Airflow, Dagster, Prefect\n Transformation Tools: dbt, SQLMesh\n AI \u0026 LLM Ecosystem: LangChain, LlamaIndex, n8n, and broader AI tooling: embedding pipelines, retrieval-augmented generation with ClickHouse as a vector store, ML feature stores, and LLM-powered data applications\n \n ClickHouse's columnar architecture and query performance make it exceptionally well-positioned in this new landscape. Your job is to make that potential real:  building the robust, production-ready connectors that make ClickHouse the natural choice when data practitioners design their next-generation AI and data systems.\n What you'll do \n \n Own and evolve ClickHouse's Python connector and SDK ecosystem, raising the bar on performance, reliability, and API design\n Build and maintain integrations with orchestration platforms (Airflow, Dagster, Prefect) and transformation tools (dbt) to enterprise-grade quality standards\n Drive the AI/LLM integration strategy:  designing connectors and patterns that make ClickHouse a natural fit in RAG architectures, ML feature pipelines, and LLM-powered data applications\n Engage actively with the open-source community: triage issues, support contributors, advocate for users, and shape the roadmap based on real-world feedback\n Collaborate with Product, Cloud, and other engineering teams to align integration work with broader platform priorities\n Bring a practitioner's perspective to roadmap decisions, grounding prioritization in genuine Data Engineer and Data Scientist workflows\n \n About you \n \n 7+ years of software development experience, including hands-on time as a Data Engineer, Data Scientist, or ML Engineer\n Deep, proven experience designing, building, and maintaining production-grade Python connectors, SDKs, or integrations for at least one major platform (orchestration, BI, MLOps, or data transformation)\n Hands-on experience applying AI/ML in production data-engineering contexts: embedding generation, vector search, feature pipelines, or LLM-powered tooling that shipped and ran in production\n Solid experience with the Python data ecosy","salary_min":157000,"salary_max":232000,"location":"United States","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"senior","tags":["mlops","embeddings","llm","agents","api-design","healthcare","data-pipeline","rag"],"apply_url":"https://job-boards.greenhouse.io/clickhouse/jobs/6107514004","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-09T12:23:47Z","expires_at":"2026-08-14T14:16:34.442006Z","created_at":"2026-07-09T14:14:22.744462Z","updated_at":"2026-07-15T14:16:34.637848Z","company_name":"ClickHouse","company_slug":"clickhouse","company_logo_url":"https://www.google.com/s2/favicons?domain=clickhouse.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/badeb1d6-7048-4b26-8dc7-7015a80bf56b"},{"id":"8e37b314-f237-44dc-a850-dd58524233c1","company_id":"19a78c6a-11dc-4d21-8273-0d2d2bad39b1","title":"Staff Data Scientist","slug":"staff-data-scientist-9bba8726","description":"Toast creates technology to help restaurants and local businesses succeed in a digital world, helping business owners operate, increase sales, engage customers, and keep employees happy.\n As a Staff Data Scientist, you’ll lead the design and development of scalable ML systems for use cases such as menu recommendation, demand forecasting, offer targeting, and guest personalization. You will serve as a technical thought partner across teams, set best practices, and influence the roadmap for ML-driven products that support key business outcomes. Your work will directly shape strategic decisions and enhance customer experience at scale.  \n This role is for a current vacancy.\n A day in the life (Responsibilities) \n \n Own the full machine learning lifecycle—from problem framing and data exploration to modeling, deployment, and monitoring—for mission-critical initiatives.\n Design and implement advanced ML and statistical models that improve product performance, operational efficiency, or customer insights.\n Collaborate with engineers, product managers, and business stakeholders to define project scope, success metrics, and integration strategy.\n Guide architectural decisions, set modeling standards, and champion best practices for experimentation, validation, and productionization.\n Mentor other data scientists and raise the technical bar through design reviews, feedback, and sharing domain expertise.\n Proactively identify areas where data science can create business value and lead cross-functional efforts to drive those opportunities forward.\n Leverage cutting edge AI tools to enhance your development workflow, improve velocity, and help pioneer new approaches to building - contributing to a culture of innovation and productivity across the team.\n \n  \n What you'll need to thrive (Requirements) \n \n 5+ years of experience in data science with a proven track record of delivering production ML systems that drive measurable impact.\n Deep knowledge of statistical modeling, machine learning (e.g., tree-based models, time series, deep learning), and model evaluation.\n Experience working with real-world product data at scale and translating ambiguous problems into well-scoped ML solutions.\n Experience with distributed data processing and training, real-time inference, and ML Ops frameworks\n Prior experience mentoring other data scientists or acting as a tech lead.\n Experience leading experimentation (e.g., A/B testing), causal inference, and real-time decision systems.\n Proficiency in Python and SQL, and experience with ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow).\n Strong grasp of software engineering principles including modular design, version control, testing, and CI/CD.\n Hands-on experience with cloud platforms (preferably AWS), including tools like SageMaker, Athena, Glue, DynamoDB, and Bedrock.\n Excellent communication skills and the ability to influence both technical and non-technical stakeholders.\n Strong business acumen with the ability to align technical solutions with company goals.\n \n Bonus ingredients* : \n \n An advanced degree in Computer Science, Statistics, or a related STEM field is preferred.\n Familiarity with MLOps tooling for monitoring, drift detection, retraining, and explainability.\n Experience fine-tuning LLMs and applying reinforcement learning from human feedback (RLHF) to improve model performance and alignment.\n \n  \n AI at Toast \n At Toast, one of our company values is that we're hungry to build and learn. We believe learning new AI tools empowers us to build for our customers faster, more independently, and with higher quality. We provide these tools across all disciplines, from Engineering and Product to Sales and Support, and are inspired by how our Toasters are already driving real value with them. The people who thrive here are those who embrace changes that let us build more for our customers; it’s a core part of our culture.\n Our Total Rewards Philosophy  We strive to provide competitive compensation and benefits programs that help to attract, retain, and motivate the best and brightest people in our industry. Our total rewards package goes beyond great earnings potential and provides the means to a healthy lifestyle with the flexibility to meet Toasters’ changing needs. Learn more about our benefits at  https://careers.toasttab.com/toast-benefits .\n #LI-Remote\n The base salary range for this role is listed below. The starting salary will be determined based on skills, experience, and geographic location. In addition to base salary, our total rewards components include cash compensation (overtime, bonus/commissions if eligible), equity, and benefits. \n Pay Range \n $127,000 — $203,000 CAD \n How Toast Uses AI in its Hiring Process \n Throughout the hiring process, our goal is to get to know you. We use AI tools to support our recruiters and interviewers with tasks like note-taking, summarization, and documentation of interviews to ensure they can be fully focus","salary_min":127000,"salary_max":203000,"location":"Canada","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["tensorflow","reinforcement-learning","deep-learning","mlops","llm","pytorch","fine-tuning","data-science"],"apply_url":"https://careers.toasttab.com/jobs?gh_jid=8052293","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-08T20:25:38Z","expires_at":"2026-08-14T14:11:50.57728Z","created_at":"2026-07-09T14:09:45.188959Z","updated_at":"2026-07-15T14:11:50.703686Z","company_name":"Toast","company_slug":"toast","company_logo_url":"https://www.google.com/s2/favicons?domain=pos.toasttab.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/8e37b314-f237-44dc-a850-dd58524233c1"},{"id":"f04f6e13-ccf2-458b-8576-e7fa94481050","company_id":"19a78c6a-11dc-4d21-8273-0d2d2bad39b1","title":"Staff Data Scientist","slug":"staff-data-scientist-317fda4d","description":"Toast creates technology to help restaurants and local businesses succeed in a digital world, helping business owners operate, increase sales, engage customers, and keep employees happy.\n As a Staff Data Scientist, you’ll lead the design and development of scalable ML systems for use cases such as menu recommendation, demand forecasting, offer targeting, and guest personalization. You will serve as a technical thought partner across teams, set best practices, and influence the roadmap for ML-driven products that support key business outcomes. Your work will directly shape strategic decisions and enhance customer experience at scale.\n A day in the life (Responsibilities) \n \n Own the full machine learning lifecycle—from problem framing and data exploration to modeling, deployment, and monitoring—for mission-critical initiatives.\n Design and implement advanced ML and statistical models that improve product performance, operational efficiency, or customer insights.\n Collaborate with engineers, product managers, and business stakeholders to define project scope, success metrics, and integration strategy.\n Guide architectural decisions, set modeling standards, and champion best practices for experimentation, validation, and productionization.\n Mentor other data scientists and raise the technical bar through design reviews, feedback, and sharing domain expertise.\n Proactively identify areas where data science can create business value and lead cross-functional efforts to drive those opportunities forward.\n Leverage cutting edge AI tools to enhance your development workflow, improve velocity, and help pioneer new approaches to building - contributing to a culture of innovation and productivity across the team.\n \n  \n What you'll need to thrive (Requirements) \n \n 7+ years of experience in data science with a proven track record of delivering production ML systems that drive measurable impact.\n Deep knowledge of statistical modeling, machine learning (e.g., tree-based models, time series, deep learning), and model evaluation.\n Experience working with real-world product data at scale and translating ambiguous problems into well-scoped ML solutions.\n Experience with distributed data processing and training, real-time inference, and ML Ops frameworks\n Prior experience mentoring other data scientists or acting as a tech lead.\n Experience leading experimentation (e.g., A/B testing), causal inference, and real-time decision systems.\n Proficiency in Python and SQL, and experience with ML frameworks (e.g., scikit-learn, PyTorch, TensorFlow).\n Strong grasp of software engineering principles including modular design, version control, testing, and CI/CD.\n Hands-on experience with cloud platforms (preferably AWS), including tools like SageMaker, Athena, Glue, DynamoDB, and Bedrock.\n Excellent communication skills and the ability to influence both technical and non-technical stakeholders.\n Strong business acumen with the ability to align technical solutions with company goals.\n Experience building services on top of LLMs in a large scale production environment.\n \n Bonus ingredients* : \n \n An advanced degree in Computer Science, Statistics, or a related STEM field is preferred.\n Familiarity with MLOps tooling for monitoring, drift detection, retraining, and explainability.\n Experience fine-tuning LLMs and applying reinforcement learning from human feedback (RLHF) to improve model performance and alignment.\n \n  \n AI at Toast \n At Toast, one of our company values is that we're hungry to build and learn. We believe learning new AI tools empowers us to build for our customers faster, more independently, and with higher quality. We provide these tools across all disciplines, from Engineering and Product to Sales and Support, and are inspired by how our Toasters are already driving real value with them. The people who thrive here are those who embrace changes that let us build more for our customers; it’s a core part of our culture.\n Our Total Rewards Philosophy  We strive to provide competitive compensation and benefits programs that help to attract, retain, and motivate the best and brightest people in our industry. Our total rewards package goes beyond great earnings potential and provides the means to a healthy lifestyle with the flexibility to meet Toasters’ changing needs. Learn more about our benefits at  https://careers.toasttab.com/toast-benefits .\n #LI-Remote\n The base salary range for this role is listed below. The starting salary will be determined based on skills, experience, and geographic location. In addition to base salary, our total rewards components include cash compensation (overtime, bonus/commissions if eligible), equity, and benefits. You can learn more about how we align pay with local labor markets in our Geographic Pay Zone Philosophy . \n Zone A\n $170,000 — $272,000 USD \n Zone B\n $148,000 — $237,000 USD \n Zone C\n $133,000 — $213,000 USD \n How Toast Uses AI in its Hiring Process \n Throughout ","salary_min":133000,"salary_max":213000,"location":"Remote (US)","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["pytorch","tensorflow","reinforcement-learning","fine-tuning","deep-learning","mlops","llm","data-science"],"apply_url":"https://careers.toasttab.com/jobs?gh_jid=8029049","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-08T20:23:10Z","expires_at":"2026-08-14T14:11:50.505714Z","created_at":"2026-07-09T14:09:45.268862Z","updated_at":"2026-07-15T14:11:50.63104Z","company_name":"Toast","company_slug":"toast","company_logo_url":"https://www.google.com/s2/favicons?domain=pos.toasttab.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/f04f6e13-ccf2-458b-8576-e7fa94481050"},{"id":"2d9fb70b-e1df-4ef9-b1ba-f021f1b7f44a","company_id":"3da82454-107f-427f-88e7-01f315ef93fb","title":"Research Engineer - Reinforcement Learning","slug":"research-engineer-reinforcement-learning-6acec267","description":"OWN YOUR INTELLIGENCE\n\n\n\nPrime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team.\n\n\n\nOur platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own.\n\n\n\nPrime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators — including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet.\n\n\n\n\nRESPONSIBILITIES\n\n - Lead and participate in novel research to build a massive scale synthetic data generation pipeline and orchestration solution\n\n - Optimize the performance, cost, and resource utilization of AI inference workloads by leveraging the most recent advances for compute \u0026 memory optimization techniques.\n\n - Contribute to the development of our open-source libraries and frameworks for synthetic data generation and distributed RL frameworks.\n\n - Publish research in top-tier AI conferences such as ICML \u0026 NeurIPS.\n\n - Distill highly technical project outcomes in layman approachable technical blogs to our customers and developers.\n\n - Stay up-to-date with the latest advancements in AI/ML infrastructure and tools, synthetic data gen research and proactively identify opportunities to enhance our platform's capabilities and user experience.\n\n\nREQUIREMENTS\n\n - Strong background in AI/ML engineering, with extensive experience in designing and implementing end-to-end pipelines for the inference or training of large-scale AI models.\n\n - Deep expertise in distributed inference techniques and frameworks (e.g. vllm, sglang) for optimizing the performance and scalability of AI workloads.\n\n - Solid understanding of MLOps best practices, including model versioning, experiment tracking, and continuous integration/deployment (CI/CD) pipelines.\n\n - Passion for advancing the state-of-the-art in reasoning and democratizing access to AI capabilities for researchers, developers, and businesses worldwide.\n\n - If you're not familiar with these, but feel like that you can contribute to our mission and you're a high-energy person, get familiar with these resources (here https://a.co/d/frW8MHY, here https://a.co/d/4WRhR0Y and here https://github.com/stas00/ml-engineering/tree/master) and please reach out!\n\n\nBENEFITS \u0026 PERKS\n\n - Cash Compensation Range of $150-350k, including equity incentives, aligning your success with the growth and impact of Prime Intellect.\n\n - Flexible work arrangements, with the option to work remotely or in-person at our offices in San Francisco.\n\n - Visa sponsorship and relocation assistance for international candidates.\n\n - Quarterly team off-sites, hackathons, conferences and learning opportunities.\n\n - Opportunity to work with a talented, hard-working and mission-driven team, united by a shared passion for leveraging technology to accelerate science and AI.\n\nWe recently raised $15mm in funding https://www.primeintellect.ai/blog/fundraise (total of $20mm raised) led by Founders Fund, with participation from Menlo Ventures and prominent angels including Andrej Karpathy (Eureka AI, Tesla, OpenAI), Tri Dao (Chief Scientific Officer of Together AI), Dylan Patel (SemiAnalysis), Clem Delangue (Huggingface), Emad Mostaque (Stability AI) and many others.\n\nIf you're excited about the opportunity to build the foundation for the future of decentralized AI and create a platform that empowers developers and researchers to push the boundaries of what's possible, we'd love to hear from you.","salary_min":150000,"salary_max":350000,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"senior","tags":["mlops","llm","agents","reinforcement-learning","search","research"],"apply_url":"https://jobs.ashbyhq.com/PrimeIntellect/ee13090e-3fea-40f0-b785-19316f52bf08/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-08T18:43:44.607Z","expires_at":"2026-08-14T14:12:06.218639Z","created_at":"2026-04-13T15:01:32.560515Z","updated_at":"2026-07-15T14:12:06.345755Z","company_name":"Prime Intellect","company_slug":"PrimeIntellect","company_logo_url":"https://www.google.com/s2/favicons?domain=primeintellect.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/2d9fb70b-e1df-4ef9-b1ba-f021f1b7f44a"},{"id":"c79e0087-291f-4c4a-a2b7-23c8d0389df7","company_id":"41d4f321-d748-4a4e-962f-dd5d23de3e43","title":"Senior Data Scientist, Systems Performance","slug":"senior-data-scientist-systems-performance-690960a2","description":"Mission Summary: \n The Systems Readiness and Performance team is the crucial bridge between software development and real-world deployment. We are responsible for driving system design, for verifying and validating the autonomy stack, and for defining, measuring, and validating system performance targets. We work closely with stakeholders in autonomy, infrastructure, and operations to build the definitive safety case for the commercial launch of our fully driverless IONIQ 5 robotaxis in Las Vegas.\n Rigorous behavioral and system performance evaluation is critical to scaling our service and achieving Motional's long-term goals. We are seeking a Senior Data Scientist to lead initiatives that improve evaluation and testing methodologies, measure the quality and trustworthiness of our evaluation portfolio, and partner with engineering teams to monitor and strengthen the health of the evaluation ecosystem. You will help ensure Motional's performance evaluation is efficient, scientifically rigorous, and aligned with our growth priorities.\n In this role, you will lead development of evaluation methodologies and metrics that assess the quality and business relevance of solutions spanning on-road and off-board data. You will influence the evaluation signals software engineers rely on to validate that changes to the autonomy stack deliver intended improvements, conduct deep-dive analyses to understand bottlenecks in current methodologies, and prototype improvements in metrics, sampling strategy, and statistical inference. You will develop deep expertise in how evaluation signals inform launch and release decisions, weigh trade-offs across the evaluation portfolio, and provide actionable insights for designing launch criteria.\n If you are a rigorous, collaborative data scientist with a passion for improving how autonomous systems are measured and validated at scale, we encourage you to apply. \n What You’ll Be Doing: \n \n Lead the development of evaluation frameworks for the autonomous system, connecting technical problems to rigorous, data-driven approaches for measuring and validating performance. \n Collaborate closely with Functional Safety and Systems Engineering teams to ensure evaluation metrics map effectively to automotive safety standards (e.g., SOTIF, ISO 21448) and launch readiness decisions.\n Ensure evaluation metrics are reliable enough to inform safety cases and launch readiness decisions.\n Monitor the reliability of evaluation metrics and incoming performance data over time, including detecting drift, inconsistencies, and degradation in metric definitions, to ensure the evaluation ecosystem remains accurate and trustworthy.\n Drive our approach to performance analysis using data-backed statistical methods for simulation and on-road data.\n Develop new statistical analysis methods to analyze AV performance data and lead by example in applying them to real problems.\n Partner with triage operators and simulation engineers to turn raw disengagements and identified edge cases into procedural or generative scenarios, feeding them back into the simulation catalog to strengthen test coverage.\n Use fleet and evaluation data to identify edge cases in an automated manner and coverage gaps, and partner with engineering to feed novel scenarios back into the simulation catalog and strengthen test coverage.\n Build confidence in the evaluation framework through data-driven insights and clear communication of findings to technical leaders and stakeholders.\n Establish correlation between on-road and simulation data to improve how we interpret and act on evaluation results.\n Make sense of large datasets to drive insights, solve ambiguous performance questions, and communicate results effectively across teams and upward to leadership.\n Establish a self-service model for developers to understand the impact of their changes.\n Develop new metrics, interpret trends, and investigate anomalies in simulation and on-road data. \n Collaborate with developers to drive action based on these results.\n Serve as an advisor and influence collaborators across multiple teams, promote data-aware decision making, and establish best practices around the use of data.\n Mentor and collaborate with fellow engineers and foster a positive, collaborative work environment.\n Introduce the use of ML methods for performance evaluation where they add rigor and scale. \n \n What You Bring: \n \n 5+ years of industry experience solving complex problems with large datasets, with a track record of framing ambiguous questions into rigorous, data-driven analyses.\n Bachelor's or higher degree in Computer Science, Computer Engineering, Data Science, Robotics, Physics, Mathematics, or a related quantitative field. Master's or PhD preferred.\n Strong problem-solving skills: ability to break down complex performance and evaluation challenges, think logically, and remove bias from how problems are defined and assessed.\n Strong Python and SQL skills, with demonstrated expe","salary_min":149000,"salary_max":198000,"location":"Remote (US)","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["mlops","deep-learning","autonomous-vehicles","robotics","data-pipeline","data-science"],"apply_url":"https://motional.com/open-positions/?gh_jid=7797913003#/7797913003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-07T17:01:19Z","expires_at":"2026-08-14T14:08:00.692798Z","created_at":"2026-07-09T14:06:16.041532Z","updated_at":"2026-07-15T14:08:00.822991Z","company_name":"Motional","company_slug":"motional","company_logo_url":"https://www.google.com/s2/favicons?domain=motional.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/c79e0087-291f-4c4a-a2b7-23c8d0389df7"},{"id":"8d2175c9-2ff5-4e92-97ef-765b6919eddc","company_id":"2721f049-2cf2-4e3e-82d0-8d8df89c8f90","title":"Forward Deployed Engineer - Physical AI Cloud Platform","slug":"forward-deployed-engineer-physical-ai-cloud-platform-5dc02f51","description":"About Nebius: \n Nebius is leading a new era in cloud infrastructure for the global AI economy. We are building a full-stack AI cloud platform that supports developers and enterprises from data and model training through to production deployment, without the cost and complexity of building large in-house AI/ML infrastructure.\n Built by engineers, for engineers. From large-scale GPU orchestration to inference optimization, we own the hard problems across compute, storage, networking and applied AI.\n Listed on Nasdaq (NBIS) and headquartered in Amsterdam, we have a global footprint with R\u0026D hubs across Europe, the UK, North America and Israel. Our team of 1,500+ includes hundreds of engineers with deep expertise across hardware, software and AI R\u0026D.\n The role \n The Forward Deployed Engineer, Cloud Platform is a senior, high-autonomy individual contributor role that owns the infrastructure foundation making the physical AI platform fast, reliable, scalable, secure, and cost-effective. This role sits with strategic customers and ISV partners, embedded directly inside their engineering teams, and ships production infrastructure that lets customers run real physical AI workloads, not just demos. Your job is to make the platform feel like a product, not a collection of cloud scripts. \n You will work alongside the Field CTO and the Head of Physical AI, and partner closely with the Physical AI Systems and Platform \u0026 Product FDEs. Inside each account, you own end-to-end technical execution: discovery, scoping, infrastructure design, build, and production rollout. Across accounts, you turn repeated infrastructure pain into reusable platform capabilities and partner with Product and Engineering to fold them into the core platform. Your field work is the primary input to the Nebius Physical AI roadmap. \n You are welcome to work remotely from the United States (SF Bay Area, CA or Austin, TX preferred).   \n Your responsibilities will include:   \n \n End-to-End Ownership Inside Strategic Accounts:  Own discovery, technical scoping, infrastructure design, build, and production rollout for each design partner and ISV engagement, translating ambiguous infrastructure problems into deployable production systems.   \n \n \n Cloud Infrastructure \u0026 Compute Orchestration:  Build and operate the cloud infrastructure that powers customer physical AI workflows. Own compute orchestration for simulation, training, evaluation, inference, and batch workloads, not just what runs, but how it runs at scale.   \n \n \n Platform Services:  Build platform services for job execution, scheduling, retries, observability, logging, secrets, access control, and cost tracking. Integrate Nebius cloud services into the product experience so infrastructure complexity is abstracted away from customers.   \n \n \n Customer Onboarding Infrastructure:  Build onboarding infrastructure for pilots, including sandbox environments, dataset storage, workflow execution, and deployment, and make sure early customer workloads run for real: secure, isolated, observable, and reliable.   \n \n \n Reliability, Security \u0026 Cost:  Optimize cloud cost, utilization, performance, and reliability across workloads, and debug infrastructure issues across application, network, storage, compute, and orchestration layers, wherever the failure actually lives.   \n \n \n Cross-FDE Partnership:  Partner with the Physical AI Systems FDE to support GPU-heavy simulation, training, and evaluation pipelines, and with the Platform \u0026 Product FDE to expose infrastructure capabilities through clean APIs, SDKs, and product workflows.   \n \n \n Long-Term Architecture:  Help define the long-term infrastructure architecture for multi-tenant SaaS, enterprise deployments, and high-throughput physical AI workloads.   \n \n \n Pattern Codification \u0026 Productization:  Turn repeated customer infrastructure pain into reusable platform capabilities. Partner with the Field CTO, Product, and Engineering teams to fold these into the core platform. Treat every engagement as a forcing function for the next ten.   \n \n \n Rapid Engineering Velocity:  Use modern AI coding tools (Claude Code, Codex, Cursor) as primary leverage. Compress build timelines from weeks to days. Treat engineering velocity as a primary success metric.   \n \n \n Field Enablement \u0026 Feedback Loops:  Co-author reference architectures, solution templates, and technical blogs for the broader Nebius field, and maintain structured channels to ensure customer learnings flow back to the Field CTO, Product, and Engineering teams.   \n \n We expect you to have:   \n \n 6+ Years of Hands-On Engineering:  Strong backend, cloud infrastructure, platform engineering, or SRE experience, with at least two years in a customer-facing or deployment-oriented technical role (Forward Deployed Engineer, founding engineer, technical co-founder, tech lead embedded with strategic customers, or equivalent).   \n \n \n Distributed Systems \u0026 Compute Platforms: ","salary_min":179500,"salary_max":224300,"location":"Remote (US)","workplace":"remote","remote_scope":"restricted","job_type":"full-time","experience_level":"lead","tags":["cloud","gpu","distributed-systems","data-pipeline","robotics","mlops"],"apply_url":"https://careers.nebius.com/?gh_jid=4875906101","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-06T20:11:20Z","expires_at":"2026-08-14T14:17:11.804949Z","created_at":"2026-07-09T14:14:52.942179Z","updated_at":"2026-07-15T14:17:11.916492Z","company_name":"Nebius","company_slug":"nebius","company_logo_url":"https://www.google.com/s2/favicons?domain=nebius.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/8d2175c9-2ff5-4e92-97ef-765b6919eddc"},{"id":"b53a52c5-c377-4c52-b8cf-a2ba46f2b9e5","company_id":"b6db41bc-ba14-4906-b2f7-a3ce9289a346","title":"Software Engineer, Early Career (AI)","slug":"software-engineer-early-career-ai-4134ec19","description":"WHO WE ARE\n\nNotion is the collaborative AI workspace where teams and agents think together https://www.youtube.com/watch?v=vkpYpWfEK5s. We're building one place where your knowledge, projects, meetings, and AI tools live side by side, so work is faster, clearer, and less fragmented. Millions of individuals, small teams, and large companies run their work on Notion.\n\n\n\nNotinos (our employees) are customer zero in bringing this future of work to life. We care about craft, building things that last, and the belief that great work is still fundamentally human. Our goal isn’t to ship the next feature. Each and every team of Notinos is working to set the standard for how humans work together in the AI era. From building a business’s system of record to making and managing AI agents to automating away the busy work, we care deeply about giving our customers more time for their life’s work.\n\n\n\n\nABOUT THE ROLE\n\n\n\nAs an engineer at Notion, you’ll help shape core user experiences and accelerate how people discover value in Notion. You'll tackle meaningful challenges with increasing autonomy, crafting code that millions of users will experience. You'll take ownership of projects that matter, make critical technical decisions, and contribute your unique perspective to our product vision. Working alongside passionate experts across design, product, and data, you'll help shape the future of how people work.\n\n\n\nWe're looking for an Early Career AI Engineer to join as a strategic partner in shaping Notion's AI vision. You'll work on cutting-edge AI-powered features, leveraging LLMs, embeddings, and other AI technologies to make Notion more intelligent and capable.\n\n\n\nThis role may be aligned to one of multiple AI-focused teams at Notion. Depending on team match and business needs, you could work on:\n\n\n\n - AI product engineering: building model-powered features end-to-end (UX, APIs, retrieval, orchestration, quality, and reliability)\n\n - Model \u0026 systems engineering: improving model integration and performance (latency, cost, safety, robustness) and building the infrastructure that supports model serving and experimentation\n\n - Evaluation \u0026 quality (evals): creating evaluation frameworks and automated/ human-in-the-loop testing to measure and improve model and product quality\n   \n   \n\n\nWHAT YOU’LL ACHIEVE\n\n - Partner with your team to prototype and ship an AI-powered product improvement\n\n - Own a scoped productionization project: integrate a new model/technique into an existing workflow, add monitoring + guardrails, or improve latency/cost/reliability.\n\n - Contribute to evals and iteration loops: build or extend an evaluation set, run experiments, analyze results, and translate learnings into product or system changes.\n\n\n\n\nSKILLS YOU'LL NEED TO BRING\n\n - You have less than two years of engineering experience. You have solid fundamentals in data structures, algorithms, and distributed systems, with a customer-minded, pragmatic approach to solving problems.\n\n - Expertise building and prototyping: You’re excited to build and iterate quickly, and you’ve started exploring AI/ML through coursework, projects, internships, or hackathons. You’re comfortable learning how different parts of a product fit together (UI, APIs, data), have some familiarity with relational databases like Postgres or MySQL, and can take an idea from prototype to a working feature with guidance.\n\n - Thoughtful problem-solving: You approach problems holistically, starting with a clear and accurate understanding of the context. You think about the implications of what you're building and how it will impact real people's lives. You can navigate ambiguity successfully, decompose complex problems into clean solutions, while also balancing the business impact of what you’re building.\n\n - Impact-driven approach to technology: You see technologies as tools to achieve user impact rather than ends in themselves. You care more about building successful systems that solve real problems than about using specific tech stacks or following trends. You stay current with the latest tools like Cursor, Claude Code, and other AI-assisted development environments, you're pragmatic about choosing the right tool for the job, focusing on what delivers the most value to users and the business.\n\n - Proactive communication and high agency: You own your work, communicating clearly about progress and blockers. You don't wait for instructions for every step but rather show initiative in identifying what needs to be done and driving projects forward. You ask questions when needed while independently finding solutions to problems.\n\n\n\n\nNICE TO HAVES\n\n - You have experience with any part of our technology stack: React, TypeScript, Node.js, Postgres, and Elasticsearch.\n\n - You care about the interaction between technology and society, the ways in which they inform each other, and our responsibility as technologists to be conscious of that relationship.\n\n - You've heard of compu","salary_min":130000,"salary_max":150000,"location":"San Francisco, CA","workplace":"remote","remote_scope":"unknown","job_type":"full-time","experience_level":"mid","tags":["mlops","distributed-systems","llm","agents","search"],"apply_url":"https://jobs.ashbyhq.com/notion/85947779-6b87-466a-98bc-30a640448c28/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-06T18:37:42.434Z","expires_at":"2026-08-14T14:04:38.422799Z","created_at":"2026-07-09T14:03:16.628186Z","updated_at":"2026-07-15T14:04:38.57202Z","company_name":"Notion","company_slug":"notion","company_logo_url":"https://www.google.com/s2/favicons?domain=notion.so\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/b53a52c5-c377-4c52-b8cf-a2ba46f2b9e5"},{"id":"035f4105-3142-414e-a11c-a881a475eff5","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Senior Software Engineer, Machine Learning Infrastructure - Generative AI","slug":"senior-software-engineer-machine-learning-infrastructure-generative-ai-451c5671","description":"About the Team \n DoorDash’s GenAI Platform team sits within Machine Learning Platform and builds the shared infrastructure that helps DoorDash, Wolt, and Deliveroo teams safely bring GenAI-powered products, agents, automation, and personalization to production. Our mission is to increase the velocity of business impact from GenAI. A central pillar of that work is running frontier open-weight LLMs and VLMs (such as GLM, Qwen, Kimi, and DeepSeek) ourselves — real-time GPU serving, high-throughput batch inference, and fine-tuning on autoscaling GPUs — delivering large cost and latency wins (for example, a billion embeddings produced roughly 20× cheaper and visual models served roughly 72% cheaper). We also own core platform surfaces including the LLM Gateway, Agent Gateway, evals infrastructure, guardrails, and cost attribution.\n About the Role \n You will join a small, high-leverage team building production infrastructure for Generative AI at DoorDash, leading the design and architecture of our open-weights model platform spanning inference and fine-tuning: real-time GPU serving, high-throughput batch inference, and model fine-tuning. You’ll set technical direction across model serving and inference engines, fine-tuning and training pipelines, GPU autoscaling and utilization, batch pipelines, backend services, and observability, and mentor engineers as you go. This role is ideal for a senior engineer who enjoys owning ambiguous, high-impact systems and pushing the cost/performance frontier of GPU inference and fine-tuning in a fast-moving technical area where product needs, model capabilities, vendor ecosystems, and cost/performance tradeoffs are evolving quickly.\n You’re excited about this opportunity because you will… \n \n Lead the design of infrastructure that helps DoorDash teams move GenAI ideas from prototype to production, increasing the velocity of business impact from AI across the company.\n Own and evolve our open-weights serving stack — real-time GPU endpoints, high-throughput batch inference, and fine-tuning (SFT/DPO/LoRA) — alongside the LLM Gateway, Agent Gateway, evals infrastructure, guardrails, and cost attribution.\n Architect scalable, high-performance systems for model serving, batch inference, GPU autoscaling, and fine-tuning that power real customer and internal automation use cases\n Push the cost and latency frontier of GPU inference — turning batch jobs that took days into hours and cutting inference cost by multiples — while giving product teams a clean choice across open-weight and closed-source models with reliability, fallback, observability, and cost controls built in.\n Build platforms that support rapid experimentation while meeting production standards for latency, scale, monitoring, SLOs, playbooks, and operational excellence.\n Partner closely with — and raise the technical bar for — ML engineers, product engineers, data scientists, and platform teams across DoorDash, Wolt, and Deliveroo to turn emerging GenAI capabilities into durable platform primitives.\n Set technical direction for the future of DoorDash’s centralized GenAI platform — including emerging directions such as reinforcement learning (RLHF/RLVR), agent optimization, and other post-training and agentic techniques — enabling the next generation of AI-powered products, agents, automation, and personalization.\n \n We’re excited about you because… \n \n B.S., M.S., or PhD. in Computer Science or equivalent\n 6+ years of industry experience in software engineering\n Deep backend engineering fundamentals, especially in Python and distributed systems.\n Track record of designing and owning production services, APIs, data pipelines, or ML infrastructure at scale.\n Experience operating systems in production, including observability, debugging, reliability, incident response, and performance/cost optimization.\n Deep hands-on experience with LLM inference and/or fine-tuning of open-weight models in production — serving (latency, throughput, batching, autoscaling, GPU utilization) and/or fine-tuning (SFT/DPO/LoRA).\n Demonstrated technical leadership: leading design across ambiguous, fast-moving technical areas, mentoring engineers, and turning customer use cases into reusable platform capabilities\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 \n Nice To Haves \n \n Experience with LLM inference engines and serving frameworks (e.g., vLLM, SGLang, TensorRT-LLM) in production\n Experience with distributed/multi-node fine-tuning and training pipelines (SFT, DPO/RLHF, LoRA), including data preparation and evaluation\n GPU performance work — multi-node/distributed inference, KV-cache/memory optimization, quantization (FP8/INT8/AWQ/GPTQ), or cold-start/throughput tuning\n Experience with Kubernetes, cloud infrastructure (AWS/GCP), GPUs, serverless/elastic GPU ","salary_min":203500,"salary_max":299300,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["generative-ai","data-pipeline","mlops","healthcare","embeddings","agents","cloud","reinforcement-learning"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8044246","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-02T18:10:05Z","expires_at":"2026-08-14T14:21:31.666489Z","created_at":"2026-07-03T14:18:26.394947Z","updated_at":"2026-07-15T14:21:31.80671Z","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/035f4105-3142-414e-a11c-a881a475eff5"},{"id":"16cc35ff-c277-4fa2-97ba-55a0048a0b21","company_id":"41d4f321-d748-4a4e-962f-dd5d23de3e43","title":"Senior Data Scientist, Systems Performance","slug":"senior-data-scientist-systems-performance-cf30584b","description":"Mission Summary: \n The Systems Readiness and Performance team is the crucial bridge between software development and real-world deployment. We are responsible for driving system design, for verifying and validating the autonomy stack, and for defining, measuring, and validating system performance targets. We work closely with stakeholders in autonomy, infrastructure, and operations to build the definitive safety case for the commercial launch of our fully driverless IONIQ 5 robotaxis in Las Vegas.\n Rigorous behavioral and system performance evaluation is critical to scaling our service and achieving Motional's long-term goals. We are seeking a Senior Data Scientist to lead initiatives that improve evaluation and testing methodologies, measure the quality and trustworthiness of our evaluation portfolio, and partner with engineering teams to monitor and strengthen the health of the evaluation ecosystem. You will help ensure Motional's performance evaluation is efficient, scientifically rigorous, and aligned with our growth priorities.\n In this role, you will lead development of evaluation methodologies and metrics that assess the quality and business relevance of solutions spanning on-road and off-board data. You will influence the evaluation signals software engineers rely on to validate that changes to the autonomy stack deliver intended improvements, conduct deep-dive analyses to understand bottlenecks in current methodologies, and prototype improvements in metrics, sampling strategy, and statistical inference. You will develop deep expertise in how evaluation signals inform launch and release decisions, weigh trade-offs across the evaluation portfolio, and provide actionable insights for designing launch criteria.\n If you are a rigorous, collaborative data scientist with a passion for improving how autonomous systems are measured and validated at scale, we encourage you to apply. \n What You’ll Be Doing: \n \n Lead the development of evaluation frameworks for the autonomous system, connecting technical problems to rigorous, data-driven approaches for measuring and validating performance. \n Collaborate closely with Functional Safety and Systems Engineering teams to ensure evaluation metrics map effectively to automotive safety standards (e.g., SOTIF, ISO 21448) and launch readiness decisions.\n Ensure evaluation metrics are reliable enough to inform safety cases and launch readiness decisions.\n Monitor the reliability of evaluation metrics and incoming performance data over time, including detecting drift, inconsistencies, and degradation in metric definitions, to ensure the evaluation ecosystem remains accurate and trustworthy.\n Drive our approach to performance analysis using data-backed statistical methods for simulation and on-road data.\n Develop new statistical analysis methods to analyze AV performance data and lead by example in applying them to real problems.\n Partner with triage operators and simulation engineers to turn raw disengagements and identified edge cases into procedural or generative scenarios, feeding them back into the simulation catalog to strengthen test coverage.\n Use fleet and evaluation data to identify edge cases in an automated manner and coverage gaps, and partner with engineering to feed novel scenarios back into the simulation catalog and strengthen test coverage.\n Build confidence in the evaluation framework through data-driven insights and clear communication of findings to technical leaders and stakeholders.\n Establish correlation between on-road and simulation data to improve how we interpret and act on evaluation results.\n Make sense of large datasets to drive insights, solve ambiguous performance questions, and communicate results effectively across teams and upward to leadership.\n Establish a self-service model for developers to understand the impact of their changes.\n Develop new metrics, interpret trends, and investigate anomalies in simulation and on-road data. \n Collaborate with developers to drive action based on these results.\n Serve as an advisor and influence collaborators across multiple teams, promote data-aware decision making, and establish best practices around the use of data.\n Mentor and collaborate with fellow engineers and foster a positive, collaborative work environment.\n Introduce the use of ML methods for performance evaluation where they add rigor and scale. \n \n What You Bring: \n \n 5+ years of industry experience solving complex problems with large datasets, with a track record of framing ambiguous questions into rigorous, data-driven analyses.\n Bachelor's or higher degree in Computer Science, Computer Engineering, Data Science, Robotics, Physics, Mathematics, or a related quantitative field. Master's or PhD preferred.\n Strong problem-solving skills: ability to break down complex performance and evaluation challenges, think logically, and remove bias from how problems are defined and assessed.\n Strong Python and SQL skills, with demonstrated expe","salary_min":149000,"salary_max":198500,"location":"Las Vegas, Nevada, United States","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["mlops","deep-learning","autonomous-vehicles","robotics","data-pipeline","data-science"],"apply_url":"https://motional.com/open-positions/?gh_jid=7792500003#/7792500003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-02T14:03:55Z","expires_at":"2026-08-14T14:08:00.928122Z","created_at":"2026-07-03T14:05:53.429797Z","updated_at":"2026-07-15T14:08:01.051833Z","company_name":"Motional","company_slug":"motional","company_logo_url":"https://www.google.com/s2/favicons?domain=motional.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/16cc35ff-c277-4fa2-97ba-55a0048a0b21"},{"id":"bc579a13-161e-4277-abd7-fbc3b1c71dc4","company_id":"41d4f321-d748-4a4e-962f-dd5d23de3e43","title":"Senior Data Scientist, Systems Performance","slug":"senior-data-scientist-systems-performance-1f0377b0","description":"Mission Summary: \n The Systems Readiness and Performance team is the crucial bridge between software development and real-world deployment. We are responsible for driving system design, for verifying and validating the autonomy stack, and for defining, measuring, and validating system performance targets. We work closely with stakeholders in autonomy, infrastructure, and operations to build the definitive safety case for the commercial launch of our fully driverless IONIQ 5 robotaxis in Las Vegas.\n Rigorous behavioral and system performance evaluation is critical to scaling our service and achieving Motional's long-term goals. We are seeking a Senior Data Scientist to lead initiatives that improve evaluation and testing methodologies, measure the quality and trustworthiness of our evaluation portfolio, and partner with engineering teams to monitor and strengthen the health of the evaluation ecosystem. You will help ensure Motional's performance evaluation is efficient, scientifically rigorous, and aligned with our growth priorities.\n In this role, you will lead development of evaluation methodologies and metrics that assess the quality and business relevance of solutions spanning on-road and off-board data. You will influence the evaluation signals software engineers rely on to validate that changes to the autonomy stack deliver intended improvements, conduct deep-dive analyses to understand bottlenecks in current methodologies, and prototype improvements in metrics, sampling strategy, and statistical inference. You will develop deep expertise in how evaluation signals inform launch and release decisions, weigh trade-offs across the evaluation portfolio, and provide actionable insights for designing launch criteria.\n If you are a rigorous, collaborative data scientist with a passion for improving how autonomous systems are measured and validated at scale, we encourage you to apply. \n What You’ll Be Doing: \n \n Lead the development of evaluation frameworks for the autonomous system, connecting technical problems to rigorous, data-driven approaches for measuring and validating performance. \n Collaborate closely with Functional Safety and Systems Engineering teams to ensure evaluation metrics map effectively to automotive safety standards (e.g., SOTIF, ISO 21448) and launch readiness decisions.\n Ensure evaluation metrics are reliable enough to inform safety cases and launch readiness decisions.\n Monitor the reliability of evaluation metrics and incoming performance data over time, including detecting drift, inconsistencies, and degradation in metric definitions, to ensure the evaluation ecosystem remains accurate and trustworthy.\n Drive our approach to performance analysis using data-backed statistical methods for simulation and on-road data.\n Develop new statistical analysis methods to analyze AV performance data and lead by example in applying them to real problems.\n Partner with triage operators and simulation engineers to turn raw disengagements and identified edge cases into procedural or generative scenarios, feeding them back into the simulation catalog to strengthen test coverage.\n Use fleet and evaluation data to identify edge cases in an automated manner and coverage gaps, and partner with engineering to feed novel scenarios back into the simulation catalog and strengthen test coverage.\n Build confidence in the evaluation framework through data-driven insights and clear communication of findings to technical leaders and stakeholders.\n Establish correlation between on-road and simulation data to improve how we interpret and act on evaluation results.\n Make sense of large datasets to drive insights, solve ambiguous performance questions, and communicate results effectively across teams and upward to leadership.\n Establish a self-service model for developers to understand the impact of their changes.\n Develop new metrics, interpret trends, and investigate anomalies in simulation and on-road data. \n Collaborate with developers to drive action based on these results.\n Serve as an advisor and influence collaborators across multiple teams, promote data-aware decision making, and establish best practices around the use of data.\n Mentor and collaborate with fellow engineers and foster a positive, collaborative work environment.\n Introduce the use of ML methods for performance evaluation where they add rigor and scale. \n \n What You Bring: \n \n 5+ years of industry experience solving complex problems with large datasets, with a track record of framing ambiguous questions into rigorous, data-driven analyses.\n Bachelor's or higher degree in Computer Science, Computer Engineering, Data Science, Robotics, Physics, Mathematics, or a related quantitative field. Master's or PhD preferred.\n Strong problem-solving skills: ability to break down complex performance and evaluation challenges, think logically, and remove bias from how problems are defined and assessed.\n Strong Python and SQL skills, with demonstrated expe","salary_min":149000,"salary_max":198500,"location":"Pittsburgh, PA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["data-pipeline","mlops","robotics","autonomous-vehicles","deep-learning","data-science"],"apply_url":"https://motional.com/open-positions/?gh_jid=7792499003#/7792499003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-02T14:03:54Z","expires_at":"2026-08-14T14:08:00.852062Z","created_at":"2026-07-03T14:05:53.340998Z","updated_at":"2026-07-15T14:08:00.980274Z","company_name":"Motional","company_slug":"motional","company_logo_url":"https://www.google.com/s2/favicons?domain=motional.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/bc579a13-161e-4277-abd7-fbc3b1c71dc4"},{"id":"36984e0e-3530-4ca8-aaf1-3139be0b6476","company_id":"41d4f321-d748-4a4e-962f-dd5d23de3e43","title":"Senior Data Scientist, Systems Performance","slug":"senior-data-scientist-systems-performance-65636409","description":"Mission Summary: \n The Systems Readiness and Performance team is the crucial bridge between software development and real-world deployment. We are responsible for driving system design, for verifying and validating the autonomy stack, and for defining, measuring, and validating system performance targets. We work closely with stakeholders in autonomy, infrastructure, and operations to build the definitive safety case for the commercial launch of our fully driverless IONIQ 5 robotaxis in Las Vegas.\n Rigorous behavioral and system performance evaluation is critical to scaling our service and achieving Motional's long-term goals. We are seeking a Senior Data Scientist to lead initiatives that improve evaluation and testing methodologies, measure the quality and trustworthiness of our evaluation portfolio, and partner with engineering teams to monitor and strengthen the health of the evaluation ecosystem. You will help ensure Motional's performance evaluation is efficient, scientifically rigorous, and aligned with our growth priorities.\n In this role, you will lead development of evaluation methodologies and metrics that assess the quality and business relevance of solutions spanning on-road and off-board data. You will influence the evaluation signals software engineers rely on to validate that changes to the autonomy stack deliver intended improvements, conduct deep-dive analyses to understand bottlenecks in current methodologies, and prototype improvements in metrics, sampling strategy, and statistical inference. You will develop deep expertise in how evaluation signals inform launch and release decisions, weigh trade-offs across the evaluation portfolio, and provide actionable insights for designing launch criteria.\n If you are a rigorous, collaborative data scientist with a passion for improving how autonomous systems are measured and validated at scale, we encourage you to apply. \n What You’ll Be Doing: \n \n Lead the development of evaluation frameworks for the autonomous system, connecting technical problems to rigorous, data-driven approaches for measuring and validating performance. \n Collaborate closely with Functional Safety and Systems Engineering teams to ensure evaluation metrics map effectively to automotive safety standards (e.g., SOTIF, ISO 21448) and launch readiness decisions.\n Ensure evaluation metrics are reliable enough to inform safety cases and launch readiness decisions.\n Monitor the reliability of evaluation metrics and incoming performance data over time, including detecting drift, inconsistencies, and degradation in metric definitions, to ensure the evaluation ecosystem remains accurate and trustworthy.\n Drive our approach to performance analysis using data-backed statistical methods for simulation and on-road data.\n Develop new statistical analysis methods to analyze AV performance data and lead by example in applying them to real problems.\n Partner with triage operators and simulation engineers to turn raw disengagements and identified edge cases into procedural or generative scenarios, feeding them back into the simulation catalog to strengthen test coverage.\n Use fleet and evaluation data to identify edge cases in an automated manner and coverage gaps, and partner with engineering to feed novel scenarios back into the simulation catalog and strengthen test coverage.\n Build confidence in the evaluation framework through data-driven insights and clear communication of findings to technical leaders and stakeholders.\n Establish correlation between on-road and simulation data to improve how we interpret and act on evaluation results.\n Make sense of large datasets to drive insights, solve ambiguous performance questions, and communicate results effectively across teams and upward to leadership.\n Establish a self-service model for developers to understand the impact of their changes.\n Develop new metrics, interpret trends, and investigate anomalies in simulation and on-road data. \n Collaborate with developers to drive action based on these results.\n Serve as an advisor and influence collaborators across multiple teams, promote data-aware decision making, and establish best practices around the use of data.\n Mentor and collaborate with fellow engineers and foster a positive, collaborative work environment.\n Introduce the use of ML methods for performance evaluation where they add rigor and scale. \n \n What You Bring: \n \n 5+ years of industry experience solving complex problems with large datasets, with a track record of framing ambiguous questions into rigorous, data-driven analyses.\n Bachelor's or higher degree in Computer Science, Computer Engineering, Data Science, Robotics, Physics, Mathematics, or a related quantitative field. Master's or PhD preferred.\n Strong problem-solving skills: ability to break down complex performance and evaluation challenges, think logically, and remove bias from how problems are defined and assessed.\n Strong Python and SQL skills, with demonstrated expe","salary_min":149000,"salary_max":198500,"location":"Boston, MA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["autonomous-vehicles","data-pipeline","deep-learning","robotics","mlops","data-science"],"apply_url":"https://motional.com/open-positions/?gh_jid=7792493003#/7792493003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-02T14:03:53Z","expires_at":"2026-08-14T14:08:00.776823Z","created_at":"2026-07-03T14:05:53.253362Z","updated_at":"2026-07-15T14:08:00.903707Z","company_name":"Motional","company_slug":"motional","company_logo_url":"https://www.google.com/s2/favicons?domain=motional.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/36984e0e-3530-4ca8-aaf1-3139be0b6476"}],"market_demand_pack":{"amount_cents":2900,"api_checkout_url":"https://aidevboard.com/api/v1/checkout?product_id=aidevboard_ai_skills_demand_pack","checkout_url":"https://aidevboard.com/market-demand-pack?qc=api-jobs-market-demand-pack\u0026utm_campaign=skills_demand_pack\u0026utm_medium=jobs_api\u0026utm_source=api","currency":"USD","description":"Full ranked public AI/ML demand CSV, source job URLs, and decision brief with market and offer angles.","fulfillment":"automatic_email_after_paid_checkout","human_checkout_url":"https://aidevboard.com/market-demand-pack?qc=api-jobs-market-demand-pack\u0026utm_campaign=skills_demand_pack\u0026utm_medium=jobs_api\u0026utm_source=api","name":"AI Market Demand Pack","next_step":"Open checkout_url for Stripe Checkout, or call api_checkout_url to get the non-charging checkout handoff payload.","price_usd":29,"product_id":"aidevboard_ai_skills_demand_pack","quote_url":"https://aidevboard.com/api/v1/quote?product_id=aidevboard_ai_skills_demand_pack"},"page":1,"per_page":20,"total":583,"total_pages":30}