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You will advance large-scale pre-training and multimodal learning across the diverse signals generated by distributed systems, including metrics, traces, logs, topology, and events. You will set the technical direction for ambitious research programs, raise the technical bar for the researchers and research engineers around you, and collaborate with Datadog's product and engineering teams to translate research advances into products. \n  \n What You'll Do: \n \n Drive research in foundation models, world models, and multimodal learning, shaping the technical direction of ambitious research programs grounded in observability\n Own research problems end to end, from framing the question through experimentation, model development, and evaluation\n Train large-scale multimodal models on diverse telemetry data, including metrics, logs, traces, topology, events, and other non-text modalities\n Advance approaches to pre-training, representation learning, world modeling, scaling, and evaluation for models that learn the dynamics of complex distributed systems\n Raise the technical bar across the team by reviewing research directions, mentoring researchers and research engineers, and setting standards for experimental rigor\n Collaborate with cross-functional teams across Research, Product, and Engineering to translate research advances into scalable Datadog capabilities\n Contribute to research publications, present at top-tier conferences such as NeurIPS, ICLR, and ICML, and help open-source key model artifacts and benchmarks\n \n  \n Who You Are: \n \n You hold a PhD in Computer Science, Machine Learning, or a related field, or have equivalent experience, with deep expertise in areas such as foundation models, world models, multimodal learning, or generative modeling\n You have driven technically ambitious research at meaningful scale as an individual contributor, whether in an industry research lab, startup, academic environment, or another research setting\n You have extensive hands-on experience designing, training, and evaluating large-scale deep learning models (such as large language models), with experience in multimodal or non-text data considered a strong plus\n You have a track record of research impact through influential publications, significant model or system contributions, widely used research artifacts, or equivalent technical achievements\n You set technical direction through influence rather than authority, and you have mentored other researchers or engineers and elevated the quality of work around you\n You want to stay deeply hands-on in research for the long term, and you can communicate complex research findings effectively across technical and non-technical audiences\n \n  \n Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog. \n  \n About Datadog:  \n Datadog (NASDAQ: DDOG) is a global SaaS business, delivering a rare combination of growth and profitability. We are on a mission to break down silos and solve complexity in the cloud age by enabling digital transformation, cloud migration, and infrastructure monitoring of our customers’ entire technology stacks. Built by engineers, for engineers, Datadog is used by organizations of all sizes across a wide range of industries. Together, we champion professional development, diversity of thought, innovation, and work excellence to empower continuous growth. Join the pack and become part of a collaborative, pragmatic, and thoughtful people-first community where we solve tough problems, take smart risks, and celebrate one another. Learn more about #DatadogLife on Instagram, LinkedIn, and Datadog Learning Center.\n  \n Equal Opportunity at Datadog: \n Datadog is an Affirmative Action and Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. Here are our Candidate Legal Notices for your reference.\n  \n Your Privacy: \n Any information you submit to Datadog as part of your application will be processed in accordance with Datadog’s Applicant and Candidate Privacy Notice.\n  \n Datadog offers a competitive salary and equity package, and may include variable compensation. Actual compensation is based on factors such as the candidate's skills, qualifications, and experience. In addition, Datadog offers a wide range of best in class, comprehensive and inclusive employee benefits for this role including healthcare, dental, parental planning, and mental health benefits, a 401(k) plan and match, paid time off, fitness rei","salary_min":320000,"salary_max":400000,"location":"New York, NY","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["distributed-systems","healthcare","deep-learning","generative-ai","pre-training","search","llm","research"],"apply_url":"https://careers.datadoghq.com/detail/8261937/?gh_jid=8261937","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-07T16:34:19Z","expires_at":"2026-11-13T16:55:38.303397Z","created_at":"2026-10-14T15:19:28.521017Z","updated_at":"2026-10-14T16:55:38.306288Z","company_name":"Datadog","company_slug":"datadog","company_logo_url":"https://www.google.com/s2/favicons?domain=datadoghq.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/bed4ba83-1003-483b-ae16-2e373644bf3e"},{"id":"ad37dc7f-806f-42da-b4c4-c27f68496615","company_id":"6ea0f41a-b13e-481a-b410-5195f391f939","title":"Sr. Partnerships Manager, US Model Labs","slug":"sr-partnerships-manager-us-model-labs-8cfd3477","description":"About the Role \n As the Sr. Partnerships Manager for US Model Labs, you will own Together AI's relationships with the most influential model builders in the United States: frontier labs, the AI research organizations inside the hyperscalers and major platform companies, chipmakers with their own model programs, and the well-funded independent labs shaping the open-weights landscape. These are the partners whose releases move the market, and your job is to make sure Together AI is the platform they launch on, build with, and route developers to.\n This is a portfolio of a small number of very large, very complex accounts. Unlike a broad ecosystem role, success here is measured in depth rather than breadth: multi-threaded relationships across research, product, legal, and business teams; deals that often bundle model distribution with compute, co-marketing, and joint go-to-market; and the trust required to get early access to models before they ship. You will sit at the intersection of Product, Engineering, Finance, and Marketing, and you will frequently be the person in the room representing Together AI to organizations many times our size.\n You are a strategic deal-maker who is comfortable in long, ambiguous cycles with sophisticated counterparties, and who can turn a single relationship into a durable, expanding partnership.\n Responsibilities \n \n Own the US lab portfolio: Build and execute the partnership strategy for a defined set of major US model labs, including account plans, executive engagement, and a clear view of each lab's roadmap and where Together AI fits in it.\n Land flagship launches: Secure day-zero (or day-one) availability of the most anticipated model releases on the Together platform, coordinating pre-release access, technical bring-up, legal, and launch marketing so we ship alongside the lab.\n Multi-thread across large organizations: Navigate research teams, product leads, developer relations, alliances and partner programs, legal, and executive sponsors to build support for Together AI at every level of the partner org.\n Structure strategic deals: Lead negotiation of distribution, licensing, revenue-share, compute, and co-marketing agreements, including non-standard structures that combine several of these. Handle complex IP, usage-rights, and exclusivity discussions with counterparties that have deep in-house legal and BD teams.\n Product and engineering alignment: Work with Product and Engineering to define bring-up requirements, manage NDA'd early access, and ensure new models are optimized for our inference and fine-tuning stacks ahead of launch.\n Commercial strategy: Partner with Finance to build deal structures that balance aggressive growth with sustainable unit economics, and to model the revenue impact of flagship model availability.\n Go-to-market and co-selling: Collaborate with Partner Marketing on joint announcements and campaigns, and work with Sales to turn lab relationships into enterprise wins through co-selling motions, joint customer engagements, and partner referral programs.\n Be the voice of the labs internally: Bring market and roadmap intelligence back to Product and Leadership, and shape how Together AI positions itself relative to the labs' own platforms and their cloud partners.\n \n Requirements \n \n Enterprise-scale partnership experience: 7–10+ years in Strategic Partnerships, Business Development, or Alliances, with a track record of closing and growing partnerships with large technology companies where the counterparty had its own BD, legal, and partner teams.\n Strategic deal-maker: You have constructed complex, multi-component agreements that don't follow a standard template, and you can hold your own in IP, licensing, revenue-share, and exclusivity negotiations with sophisticated counterparties.\n High EQ and executive presence: You build trust quickly, manage long relationships through changes in personnel and priorities, and are credible in front of senior leaders at partner organizations.\n Technical fluency: While you don't need to be an ML researcher, you should be able to discuss model architectures, context windows, licensing terms, and inference and fine-tuning requirements with both partners and our internal teams.\n AI-native mindset: You work at pace with a relentless sense of urgency, and you prefer \"done and iterating\" over \"perfect and stagnant,\" even while managing accounts that move on their own timelines.\n \n Bonus Points\n \n Direct experience working at, or partnering with, a frontier lab, a hyperscaler AI organization, or a major open-weights model builder.\n Experience with hyperscaler or cloud marketplace partner programs (e.g., alliances, co-sell, or ISV programs).\n Experience in co-selling motions where partnerships directly drove revenue.\n Background at a high-growth API, cloud infrastructure, or developer platform company managing complex ecosystems.\n \n About Together AI \n Together AI, the AI Native Cloud, is purpose-built for AI engineers","salary_min":300000,"salary_max":350000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["cloud","reinforcement-learning","pre-training","fine-tuning"],"apply_url":"https://job-boards.greenhouse.io/togetherai/jobs/5257509007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-10-06T19:36:43Z","expires_at":"2026-11-13T16:55:08.950384Z","created_at":"2026-10-14T15:18:58.555014Z","updated_at":"2026-10-14T16:55:08.953226Z","company_name":"Together AI","company_slug":"together-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=together.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/ad37dc7f-806f-42da-b4c4-c27f68496615"},{"id":"58ceddcf-ed90-4558-9bdc-c1faab46fc2e","company_id":"6ea0f41a-b13e-481a-b410-5195f391f939","title":"Staff Software Engineer - AI Compute, Together Cloud","slug":"staff-software-engineer-ai-compute-together-cloud-60abdd08","description":"About the Role \n Together AI is building the AI Native Cloud, an end-to-end platform for the full generative AI lifecycle, combining the fastest LLM inference engine with state-of-the-art GPU cloud infrastructure. The Together Cloud team builds the [Together GPU Clusters](https://www.together.ai/gpu-clusters) flagship IaaS product that provides high-performance, AI-ready GPU clusters through a self-serve cloud console, along with the virtualized infrastructure layer powering Together's inference, RL, and fine-tuning products.\n As a Staff Software Engineer focusing on AI Compute in the Together Cloud org, you will set technical direction for and build major components of the next generation AI cloud platform – a highly available, global cloud infrastructure with cutting-edge virtualization of the latest ML hardware: GB300s/VRs, BlueField DPUs, InfiniBand and dual/quad-plane RoCEv2 fabrics. That virtualized computing platform powers our own SaaS products – inference, RL, and fine-tuning – and serves external cloud customers through self-serve offerings such as on-demand/reserved Kubernetes/Slurm clusters, across dozens of data centers and hundreds of thousands of GPUs.\n This is an architect-and-build role. Fully automated bootstrapping of GPU data centers, high-performance virtualization of GPU compute and DC networking without compromising isolation or portability, and fault-tolerant decentralized control planes — you'll set the architecture for these across our global and in-DC services, and be a key owner of the hardest parts, in the code as well as the design. Your designs will span the IaaS layer of a greenfield Vera Rubin data center up to the global management plane that schedules capacity across all of them. At this level the job is as much leverage as code: the standards you set and the engineers you grow decide how fast the rest of Together Cloud ships.\n Responsibilities \n \n Own the GPU and network virtualization stack: the hypervisor, kernel, and SDN work that keeps GPU compute and DC networking high-performance, portable, and strongly isolated across heterogeneous hardware.\n Own the in-DC IaaS layer: architect and roadmap the services, Kubernetes operators, and libraries that provision and manage compute, storage, and networks in our data centers — VMs, parallel filesystems, VPCs, and InfiniBand partitions; lead its build-out for a new Vera Rubin data center with thousands of GPUs, from hardware bring-up to customer-facing API.\n Design the GPU scheduling and global management plane: the distributed control plane behind on-demand and reserved clusters across dozens of data centers, including the systems that scale per-cluster limits and automate the onboarding of new capacity.\n Architect monitoring and automated remediation for fault tolerance: the strategy for automated detection, isolation, and recovery of failed nodes that keeps distributed pretraining and large-scale inference running through hardware failures.\n Set technical direction across teams: lead design reviews, resolve cross-cutting architectural disagreements, unblock cross-team dependencies and integration risks, and define the standards other engineers build against — measured in cluster reliability, time-to-first-GPU on new capacity, and quality at scale.\n Grow the team: mentor senior and junior engineers, deepen the team's expertise in virtualization, DC networking, and GPU infrastructure, and help raise the hiring bar for Together Cloud.\n Set the engineering bar: create the testing frameworks, tools, and developer documentation that make our systems robust and usable by other teams, and shape the core, open-source Together AI platform.\n \n To be successful you'll need to be deeply technical and an excellent communicator — expert software development fundamentals, deep systems knowledge and troubleshooting instincts, and the leadership and diplomacy skills to align teams that don't report to you. Much of this work starts ambiguous, and we expect you to define the scope yourself and drive it to production.\n Requirements \n \n 7+ years of professional software development experience, with expert-level proficiency in at least one backend language (Golang desired), writing high-performance, well-tested, production-quality code.\n Track record of owning the architecture of large distributed systems from blank page to production at scale, including the judgment calls that could not be reversed cheaply.\n Deep experience building and operating globally distributed, high-performance microservice architectures across one or more cloud providers (AWS, Azure, GCP).\n Expert systems knowledge across compute, networking, and storage — including concurrency, memory management, performant I/O, and scale at a global level.\n Demonstrated technical leadership beyond your own commits: mentoring senior engineers, leading design reviews, and driving alignment across teams that do not report to you.\n Excellent communication and diplomacy skil","salary_min":260000,"salary_max":300000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["gpu","fine-tuning","pre-training","distributed-systems","generative-ai","llm","microservices","cloud"],"apply_url":"https://job-boards.greenhouse.io/togetherai/jobs/5224090007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-23T22:48:12Z","expires_at":"2026-11-13T16:55:08.972984Z","created_at":"2026-10-14T15:18:58.57614Z","updated_at":"2026-10-14T16:55:08.975679Z","company_name":"Together AI","company_slug":"together-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=together.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/58ceddcf-ed90-4558-9bdc-c1faab46fc2e"},{"id":"7086496a-58c5-4d58-a745-6582d5cf6694","company_id":"a0000000-0000-0000-0000-000000000001","title":"Software Engineer, Tokens and Prompt Structures","slug":"software-engineer-tokens-and-prompt-structures-0f6e4567","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the Role: \n The Encodings Infra team maintains the libraries that engineers and researchers across Anthropic use to encode text and multimodal data into a form that Claude can consume. It also determines Claude’s prompt shape: how a user’s turn is represented to the model, how Claude calls tools and receives tool results, and so on.\n As a Software Engineer on this team, you'll own the design and maintenance of these libraries—keeping their APIs intuitive, their performance sharp, and their abstractions solid enough that most of the org never has to think about encodings or prompt structures at all. You’ll have the satisfaction of knowing that your work enabled Claude to learn new ways of understanding the world.\n This role is unusually broad: your work will touch systems across the codebase, from pretraining to finetuning to the API, and you'll collaborate closely with both researchers and engineers to make sure new encoding ideas can move quickly from experiment to production.\n Responsibilities: \n \n \n Maintain and improve the encoding libraries used by engineers and researchers across Anthropic\n \n Run experiments to determine the optimal way to feed structured data into Claude without confusing it\n \n Design data structures and abstractions that shield most of the organization from the details of how encoded data works while enabling “power users”\n \n Adapt the encoding libraries to support new research directions as they emerge, and make sure that we can ship these research ideas to production\n \n Optimize encoding performance across the systems that depend on these libraries\n \n You may be a good fit if you: \n \n \n Have 5+ years of software engineering experience, with meaningful time spent maintaining libraries, SDKs, or developer-facing APIs\n \n Have familiarity with ML terminology and LLM architecture — you don't need to be an ML expert, but enough understanding to work effectively alongside researchers and understand the results of experiments\n \n Have experience carrying out complex refactors in large codebases\n \n Have strong communication skills and enjoy working closely with researchers and engineers to understand what they need\n \n Are results-oriented, with a bias towards flexibility and impact\n \n Pick up slack, even if it goes outside your job description\n \n Care about the societal impacts of your work\n \n Strong candidates may also have experience with: \n \n \n Tokenizers or other text/data encoding systems\n \n Maintaining a widely-used library over a long period of time\n \n Performance optimization\n \n Python and/or Rust\n \n Reinforcement learning or model training infrastructure\n \n Representative projects: \n \n \n Working with a research team to ship a new multimodal data type (audio, video, etc) to production\n \n Redesigning a core abstraction so that we can change how data is encoded into Claude without breaking downstream teams\n \n  \n The annual compensation range for this role is listed below. \n For sales roles, the range provided is the role’s On Target Earnings (\"OTE\") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.\n Annual Salary:\n $320,000 — $405,000 USD \n Logistics \n Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience\n Required field of study:  A field relevant to the role as demonstrated through coursework, training, or professional experience\n Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position\n Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.\n Visa sponsorship:  We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.\n We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed.  Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, ","salary_min":320000,"salary_max":405000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["pre-training","llm","fine-tuning","reinforcement-learning","alignment","research"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5421263008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-16T14:12:43Z","expires_at":"2026-11-13T16:54:56.564881Z","created_at":"2026-10-14T15:17:23.037884Z","updated_at":"2026-10-14T16:54:56.567934Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/7086496a-58c5-4d58-a745-6582d5cf6694"},{"id":"0ae6c507-ddde-438c-8fc3-912039107ae9","company_id":"6ea0f41a-b13e-481a-b410-5195f391f939","title":"Technical Marketing Engineer","slug":"technical-marketing-engineer-896159f2","description":"About The Role \n Most of what makes Together AI so valuable for customers is invisible from the outside. Optimizing kernels, scheduling, quantization, speculative decoding, and more create an industry-leading platform for inference and post-training at scale. As a technical marketing engineer, you are the conduit between the engineering and research work and the developers who should know about it. You go deep with product, engineering, research, and CX to learn the platform at the level of nuanced technical detail, then decide which details matter to an AI builder and build content that makes them care.\n This is a creator role. You are a technical storyteller. You'll produce social-native posts, technical blogs, performance benchmarks, demo and explainer videos, and thought leadership, all aimed at one outcome: building Together’s credibility with developers. You'll also own our technical communities on X, YouTube, Discord, Linkedin and the engagement metrics for developer content company-wide, including amplifying the work of engineers and researchers who publish under their own names.\n Responsibilities \n \n Partner with product, engineering, DevRel, research, and CX to learn new capabilities in technical depth, then extract the most meaningful aspects for a developer audience.\n Create the content strategy based on deep understanding of our company’s differentiation and marries that with an understanding of the content that would resonate.\n Build technical content across formats: blogs, videos, benchmark reports, podcasts, and short-form posts that drive engagement with developers and preference for Together.\n Monitor real-time news and announcements related to AI infrastructure and lead timely responses working with product and engineering\n Own Together's presence in technical communities, primarily X, Discord and Youtube. Track engagement metrics, what lands, what doesn't, and adjust.\n Drive amplification of engineering and research work across social channels.\n Be the heartbeat of the Together AI technical community - understanding the developers and researchers who follow us and enabling them to be successful\n Experiment and test new ideas to see what resonates with the community\n While the role involved mainly online engagement (80%), there is an aspect of translating their deep understanding to deliver highly engaging in-person meet ups and events (20%) in collaboration with Events/ DevRel teams\n \n Requirements  \n \n 3+ years of product, technical or developer marketing\n Experience creating content for infrastructure or technical systems, not user-facing SaaS applications. You've explained how something works under the hood.\n Loves building social community presence, posting, engaging, monitoring with technical audiences, with experience managing channels\n Technical depth sufficient to hold a real conversation with engineers about architecture, performance, and tradeoffs, and to write about it accurately (partnering with SMEs).\n A portfolio of technical content you created with the metrics that show engagement.\n Familiarity with AI infrastructure: inference, fine-tuning, GPU compute, model serving, or adjacent systems (including databases, data infrastructure, or storage infrastructure).\n Hands-on experience running and interpreting performance benchmarks.\n Fluency with developer communities on social platforms, and judgment about what earns engagement there versus what gets ignored.\n Innate curiosity about how things work, and the instinct to show the market what you find.\n Strong ownership mindset and ability to build something from ground up\n Data-driven and ideas person - always thinking of new experiments and content\n \n About Together AI \n Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, reinforcement learning to create specialized models, and pre-training for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves over 400 trillion tokens a month.\n Compensation \n We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $200K-230K + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge. This is a hybrid role based in the Bay Area.  \n Equal Opportunity \n Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.\n Please see our privacy policy at https://www.together.ai/privacy","salary_min":200000,"salary_max":230000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["mlops","pre-training","fine-tuning","reinforcement-learning"],"apply_url":"https://job-boards.greenhouse.io/togetherai/jobs/5238633007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-15T23:55:21Z","expires_at":"2026-11-13T16:55:09.002178Z","created_at":"2026-10-14T15:18:58.601622Z","updated_at":"2026-10-14T16:55:09.006559Z","company_name":"Together AI","company_slug":"together-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=together.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/0ae6c507-ddde-438c-8fc3-912039107ae9"},{"id":"ecb2722a-9602-49a6-b061-016925fd3ac3","company_id":"6ea0f41a-b13e-481a-b410-5195f391f939","title":"Senior Software Engineer — Infra Agent Systems","slug":"senior-software-engineer-infra-agent-systems-4dbe0090","description":"About the Role \n Together AI runs one of the largest GPU fleets in the world. The Infra Agent Systems team builds the software systems that power and automate that infrastructure.\n We develop production AI agents that diagnose hardware failures, investigate incidents, correlate signals across the fleet, and automate operational workflows. Alongside these agents, we build the platform they run on, including knowledge graphs, retrieval systems, orchestration frameworks, and developer tooling.\n You’ll work across two areas:\n Infrastructure Agent Systems — Build production AI agents that help operate our GPU fleet by diagnosing failures, investigating incidents, gathering evidence from live systems, and assisting with remediation. These agents are used every day by our infrastructure and datacenter teams through APIs, CLI, dashboards, and Slack.\n Core Agent Platform — Build the platform that powers these agents, including knowledge graphs, search and retrieval, orchestration, evaluation, and the tooling that enables agents to reason, act, and continuously improve.\n We’re working on something that hasn’t really been done before: building knowledge graphs and self-improving AI agents that understand, operate, and continuously improve large-scale AI infrastructure.\n This is an opportunity to work at the intersection of AI agents, distributed systems, infrastructure, and automation , solving challenging engineering problems with real production impact. There’s an enormous amount to build, learn, and shape as we define the future of autonomous infrastructure.\n Why this Role \n You’ll work on two hard problems at the same time: making AI agents trustworthy enough to operate production infrastructure, and building the knowledge, retrieval, and distributed systems that make those agents effective.\n You’ll have the opportunity to build foundational systems from the ground up, work on infrastructure at massive scale, and help define how self-improving AI agents operate real-world AI infrastructure.\n Responsibilities \n \n Design and build production AI agent systems that diagnose, investigate, and remediate infrastructure issues across one of the world’s largest GPU fleets.\n Build the distributed services, orchestration framework, knowledge graph, and retrieval systems that power infrastructure agents.\n Develop fleet intelligence systems that combine telemetry, infrastructure state, operational knowledge, and historical incidents to help agents make better decisions.\n Integrate with observability, incident management, ticketing, fleet inventory, source control, chat, and internal infrastructure systems through well-designed APIs.\n Own services end to end, including architecture, implementation, testing, deployment, observability, and production operations.\n Improve agent performance through evaluations, retrieval improvements, better tools, and production feedback loops.\n Turn what agents learn in production into reliable, reviewed software and automation.\n \n Requirements \n \n 5+ years of experience building production backend systems, distributed systems, or infrastructure platforms.\n Strong systems design skills and experience owning significant systems from design through production.\n Depth in at least one of the following:\n \n AI agent systems, orchestration, tool use, evaluation, or grounding\n Knowledge graphs or graph data modeling\n Search, retrieval, ranking, RAG, or semantic search systems\n \n Strong backend engineering experience, including API design, service boundaries, data modeling, and integrations across complex systems.\n Experience with Kubernetes, GitOps such as ArgoCD, infrastructure-as-code, and cloud platforms.\n Comfortable working across languages such as Go, TypeScript, Python, or Rust.\n \n Experience in the following is a plus:\n \n GPU infrastructure, datacenters, bare-metal systems, hardware failure modes, BMC/IPMI, or cluster schedulers\n Graph databases\n Event-driven systems and messaging platforms such as NATS or Kafka\n Observability platforms such as Prometheus and Grafana\n Building evaluation frameworks or improving the quality and reliability of LLM-powered systems\n \n About Together AI \n Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month.\n Compensation \n We offer competitive compensation, startup equity, health insurance, and other benefits, as well as flexibility in terms of remote work. The US base salary range for this full-time position is: $250,000 - $300,000 + equity + benefits. Our salary ranges are determined by location, level and role. Indiv","salary_min":250000,"salary_max":300000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["search","distributed-systems","reinforcement-learning","api-design","pre-training","agents","gpu","llm"],"apply_url":"https://job-boards.greenhouse.io/togetherai/jobs/5233669007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-10T22:30:49Z","expires_at":"2026-11-13T16:55:08.924992Z","created_at":"2026-09-12T13:32:10.296505Z","updated_at":"2026-10-14T16:55:08.927753Z","company_name":"Together AI","company_slug":"together-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=together.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/ecb2722a-9602-49a6-b061-016925fd3ac3"},{"id":"52be8ff9-e16f-44a0-bd16-3906ee2be328","company_id":"83c597c2-a4b2-4517-99df-1ac8c90756d5","title":"Staff, Machine Learning Engineer - BEV/Multi-Modal Perception","slug":"staff-machine-learning-engineer-bevmulti-modal-perception-ed2d5016","description":"About the Company  \n At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business.\n A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners.  Now a part of the Daimler family , we are focused solely on developing software for automated trucks to transform how the world moves freight. \n Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer. \n Meet the Team As a Staff Machine Learning Engineer specializing in BEV (Bird's-Eye View) and Multi-Modal Perception, you will lead the development of next-generation models that unify information across cameras, LiDAR and radar to deliver a rich spatial understanding of the driving environment. You will drive architectural innovation, large-scale model training, and data-driven improvements that directly advance the perception capabilities at the heart of Torc's autonomous driving stack. This is a technical leadership role focused on model innovation and maturity, not downstream feature integration.\n What You'll Do \n \n Lead BEV model development: define and execute the technical roadmap for BEV-based perception models across multiple tasks (e.g., detection, segmentation, road topology, and scene understanding).\n Design advanced multi-modal architectures that fuse heterogeneous sensor data (camera, LiDAR, radar, HD maps) into unified spatial representations.\n Develop foundational perception models leveraging BEV transformers, voxel-based encoders, or implicit scene representations.\n Own large-scale training workflows — from data sampling strategies and augmentation pipelines to distributed training and hyperparameter optimization.\n Advance model robustness and generalization, addressing long-tail conditions such as low visibility, occlusions, and rare scene configurations.\n Establish evaluation frameworks for geometric accuracy, temporal stability, and cross-domain transfer performance.\n Collaborate cross-functionally with sensor calibration, mapping, and fusion teams to ensure cohesive perception model interfaces.\n Mentor and guide ML engineers, cultivating best practices in experimentation, code quality, and model validation.\n Stay at the forefront of ML research, exploring self-supervised learning, large-scale pretraining, or foundation models for 3D perception.\n \n What You'll Need to Succeed \n \n 10+ years of experience in deep learning for perception, 3D vision, and/or autonomous systems.\n M.S. or Ph.D. in Computer Science, Electrical Engineering, Robotics, or related field (or equivalent practical experience).\n Proven expertise in BEV modeling, 3D scene understanding, and multi-view fusion.\n Strong background in multi-modal sensor fusion, particularly integrating camera and LiDAR data.\n Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.\n Experience with large-scale data pipelines, distributed training, and experiment management systems.\n Demonstrated leadership in driving ML model innovation and mentoring technical teams.\n \n Bonus Points \n \n Experience with autonomous driving or robotics perception in production environments.\n Experience with MLOps and infrastructure tools (Ray).\n Hands-on expertise in BEV-based ML architectures, LiDAR-vision fusion, or spatial-temporal modeling.\n Familiarity with 3D labeling, calibration, and sensor simulation pipelines.\n Track record of publications or open-source contributions in top-tier venues (CVPR, ICCV, NeurIPS, ICRA, CoRL).\n Understanding of performance tradeoffs and deployment constraints (latency, memory, accuracy).\n \n Work Location: For this position, we are open to hiring in Ann Arbor, MI in a hybrid capacity. We are also open to hiring Remote in the United States.\n Perks of Being a Full-time Torc’r  \n Torc cares about our team members and we strive to provide benefits and resources to support their health, work/life balance, and future. Our culture is collaborative, energetic, and team focused. Torc offers:   \n \n A competitive compensation package that includes a bonus component and stock options\n 100% paid medical, dental, and vision premiums for full-time employees   \n 401K plan with a 6% employer matchFlexibility in schedule and generous paid vacation (available immediately after start date)Company-wide holiday office closures\n AD+D and Life Insurance  \n \n At Torc, we’re committed to building a diverse and inclusive workplace. We celebrate the uniqueness of our Torc’rs and do not discriminate based on race, religion, color, national origin, gender (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity, gender expression, age, veteran status, or disabilities. \n Even if you don’t meet 100% of the qualifications listed for this opportun","salary_min":215500,"salary_max":258600,"location":"Ann Arbor, MI","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["tensorflow","generative-ai","deep-learning","autonomous-vehicles","pre-training","payments","data-pipeline","pytorch"],"apply_url":"https://job-boards.greenhouse.io/torcrobotics/jobs/8720904002","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-04T16:52:57Z","expires_at":"2026-11-13T16:56:17.814112Z","created_at":"2026-09-05T13:35:28.134734Z","updated_at":"2026-10-14T16:56:17.817249Z","company_name":"Torc Robotics","company_slug":"torc-robotics","company_logo_url":"https://www.google.com/s2/favicons?domain=torc.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/52be8ff9-e16f-44a0-bd16-3906ee2be328"},{"id":"04da9126-05bb-40a1-a1d3-1dbb394f7d5d","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Staff Tech Lead, Perception, Pre-training \u0026 Distillation","slug":"staff-tech-lead-perception-pre-training-distillation-15e566c4","description":"Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n The Perception team builds the system which learns the spatial-temporal representation and their semantic meanings of the surrounding environment of the self-driving car, i.e., the system that “perceives” the world around the car. We work jointly with downstream teams on the optimization and integration into the Waymo Driver. We conduct our own research to address real-world problems and collaborate with research teams at Alphabet. We have access to millions of miles of driving data from a diverse set of sensors, enabling engineers like you to (1) develop methods for efficiently learning from large scale real-world data, to (2) develop models and model training at scale, to (3) analyze real-world behavior and develop systems for handling the complexities of interacting with the real-world\n In this role you will report to a Sr Staff Technical Lead Manager.\n You will: \n \n Train multi-billion parameter spatio-temporal foundation models on multi-billion frame data sets.\n Deliver pre-trained models for production usage, ideally for usage in real-time systems.\n Develop self-supervised learning, including image/video encoder, 3D point cloud encoder, spatio-temporal reconstruction or future prediction, and vision-language contrastive learning.\n Develop multi-teacher distillation recipes.\n Develop methods to understand and evaluate the quality of latent representations from pre-training. Collaborate with the post-training team to ensure pre-training delivers the world knowledge representation needed for the multitude of post-training tasks.\n Develop methods to curate and effectively use diverse datasets in training.\n Be a key contributor to technical roadmaps.\n \n You have: \n \n MS or PhD in Computer Science or a similar discipline, or an equivalent amount of deep learning experience\n 5+ years experience in Machine Learning and/or Computer Vision\n Fluent in Python\n In-depth experience with ML frameworks like PyTorch or JAX\n \n We Prefer: \n \n Publications at top-tier conferences like CVPR, ICCV, ECCV, ICLR, ICML, ICRA, RSS, NeurIPS, AAAI, IJCV, PAMI\n Experience with C++\n Experience with real-time system design\n The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.  \n Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.  \n Salary Range\n $251,000 — $310,000 USD","salary_min":251000,"salary_max":310000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["deep-learning","computer-vision","generative-ai","autonomous-vehicles","pre-training","pytorch"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=8172377","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-09-01T23:46:00Z","expires_at":"2026-11-13T16:55:55.292922Z","created_at":"2026-09-02T13:35:12.37077Z","updated_at":"2026-10-14T16:55:55.296342Z","company_name":"Waymo","company_slug":"waymo","company_logo_url":"https://www.google.com/s2/favicons?domain=waymo.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/04da9126-05bb-40a1-a1d3-1dbb394f7d5d"},{"id":"cd983088-fdcd-4ead-991a-d7deeaa13f5c","company_id":"6ea0f41a-b13e-481a-b410-5195f391f939","title":"Manager, International Cloud Sourcing","slug":"manager-international-cloud-sourcing-35477aa9","description":"About the Role \n Together AI is looking for a Manager to own the commercial relationships with our international cloud compute suppliers and the market intelligence that drives our compute supply strategy. You will manage the lifecycle of international cloud service provider (CSP) contracts and renewals, run RFP processes for new capacity, build pricing benchmarks across the global GPU cloud landscape, and partner with infrastructure engineering, finance, and GTM to secure the right capacity at the best economics as we rapidly scale our GPU fleet globally. \n Responsibilities \n \n Manage the lifecycle of international CSP leasing agreements, from sourcing and RFPs through contract execution and renewals\n Partner with infrastructure engineering to translate technical requirements into commercial specifications, and coordinate technical evaluations during vendor selection\n Develop and maintain relationships with cloud compute suppliers to expand Together's vendor ecosystem into new international markets\n Build and maintain market intelligence to stay ahead of pricing trends, supply availability, and the evolving international cloud compute vendor landscape\n Develop negotiation strategies that improve our cost position and terms as we scale, accounting for local contracting norms and market dynamics\n Drive continuous improvement on Total Cost of Ownership (TCO) and actively mitigate supply chain and commercial risks across the GPU fleet\n Establish and lead regular, structured vendor performance reviews, including Quarterly Business Reviews (QBRs), with international cloud and infrastructure compute suppliers.\n Track the country-level factors that shape international capacity decisions, including power availability, local regulatory and data residency requirements, currency, and tax and duty implications\n \n Requirements \n \n 5+ years in cloud sourcing, strategic sourcing, or infrastructure commercial roles, with exposure to GPU or high-performance compute environments\n Track record negotiating cloud or infrastructure contracts (multi-million dollar deal sizes), including with suppliers outside the US\n Working knowledge of cloud infrastructure, including GPU configurations, networking, and cluster architecture, with the ability to translate technical requirements into commercial terms\n Strong analytical skills; experience building pricing models or financial analyses that inform sourcing decisions\n Excellent communication skills; comfortable working across engineering, finance, and vendor executives, including international suppliers, and available for calls outside standard US business hours\n Ability to apply FinOps principles (e.g., cost allocation, rightsizing, commitment strategy) to cloud sourcing and contract negotiations to drive demonstrable cost efficiency across the GPU fleet.\n \n Nice to Have \n \n Experience in GPU/AI compute sourcing specifically, or at a GPU cloud provider or hyperscaler\n Familiarity with the AI infrastructure vendor ecosystem and current supply/demand dynamics, particularly outside the US\n Experience building sourcing processes from scratch rather than operating within established systems\n Familiarity with data center economics, including power, cooling, and site selection considerations, and with country-level factors such as data residency, currency, and duties\n \n About Together AI \n Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month.\n Compensation \n We offer competitive compensation, startup equity, health insurance, and other benefits, as well as flexibility in terms of remote work. The US base salary range for this full-time position is: $190-220K + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.\n Equal Opportunity \n Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.\n Please see our Privacy Policy at  https://www.together.ai/privacy","salary_min":190000,"salary_max":220000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"junior","tags":["fine-tuning","pre-training","cloud","reinforcement-learning","infrastructure"],"apply_url":"https://job-boards.greenhouse.io/togetherai/jobs/5211627007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-18T23:57:54Z","expires_at":"2026-11-13T16:55:08.829557Z","created_at":"2026-10-14T15:18:58.442736Z","updated_at":"2026-10-14T16:55:08.832383Z","company_name":"Together AI","company_slug":"together-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=together.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/cd983088-fdcd-4ead-991a-d7deeaa13f5c"},{"id":"0b8db96a-3d3f-42a3-af01-1ba23f5bfb06","company_id":"6ea0f41a-b13e-481a-b410-5195f391f939","title":"Senior Product Manager, Model APIs \u0026 Developer Experience","slug":"senior-product-manager-model-apis-developer-experience-41e07313","description":"About the Role \n Together serves a broad portfolio of open-weight models, backed by infrastructure designed to run them quickly and efficiently. The interfaces around those models shape the entire customer experience: how easily a developer can migrate an application, how reliably an agent can use a model, and how a team runs large asynchronous workloads.\n As Senior Product Manager for Model APIs and Developer Experience, you will own those interfaces. Your scope will extend beyond chat to include the APIs and developer experience for image, video, voice, and passthrough models. You will also own compatibility with the wider developer and agent ecosystem, the voice consumption experience and associated model partnerships, and the evolution of our batch inference product.\n These areas are connected by a single goal: make Together's models easy to adopt and reliable to build on for developers and autonomous agents.\n This is a hands-on product role. You will read API specifications, test integrations, inspect code when useful, and build prototypes or demo applications to sharpen your thinking. You will work closely with engineering and research, own the product direction, make thoughtful tradeoffs, ship, and learn from how customers use what we build.\n Responsibilities \n \n Set the product direction and roadmap across chat and multimodal APIs, ecosystem compatibility, voice, and batch inference.\n Close the API and behavioral gaps that make it harder for customers to move workloads from proprietary model providers to Together.\n Develop a grounded view of how Together's APIs perform in the developer and agent ecosystem, and turn the most important gaps into clear product priorities.\n Define an intuitive API and consumption experience for real-time and agentic voice applications.\n Build productive relationships with voice model partners and align internal and external teams around a strong joint product experience.\n Reimagine batch inference across the job lifecycle, developer experience, completion guarantees, pricing, and packaging.\n Build lightweight prototypes and demo applications to test product ideas and reduce uncertainty before committing significant engineering resources.\n Work closely with engineering and research to understand technical constraints, make product tradeoffs, and deliver reliable customer experiences.\n Talk with customers and study product usage to separate isolated requests from patterns that should shape the platform.\n Create a clear, active roadmap and communicate why we are making specific investments and what we expect them to change.\n \n Requirements \n \n Meaningful experience with APIs and developer tools, whether you designed them, built them, or owned them as a product manager.\n Strong product judgment and the ability to make clear decisions in an evolving market.\n A willingness to get close to the work by reading specifications and code, testing integrations directly, and building working prototypes with AI development tools.\n A track record of earning trust with engineering and external partners through preparation, sound technical judgment, clear communication, and follow-through.\n A bias toward experimentation and iteration. You know how to gather enough evidence to make a decision, ship, and adjust based on what you learn.\n Strong communication and relationship-building skills, including the ability to align teams and move work forward across company boundaries..\n \n About Together AI \n Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month.\n Compensation \n We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $200 - 280k + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.\n Equal Opportunity \n Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.\n Please see our privacy policy at https://www.together.ai/privacy .","salary_min":200000,"salary_max":280000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","reinforcement-learning","pre-training","fine-tuning"],"apply_url":"https://job-boards.greenhouse.io/togetherai/jobs/5210951007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-17T17:58:36Z","expires_at":"2026-11-13T16:55:08.907225Z","created_at":"2026-08-25T18:26:43.642838Z","updated_at":"2026-10-14T16:55:08.909923Z","company_name":"Together AI","company_slug":"together-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=together.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/0b8db96a-3d3f-42a3-af01-1ba23f5bfb06"},{"id":"c329b188-7612-4641-ac6d-796f7502eb3d","company_id":"c587b06c-b6f0-4d1d-b694-6fb6abc2a6bb","title":"Senior Research Engineer, LLM Training \u0026 Post-Training","slug":"senior-research-engineer-llm-training-post-training-b05c8bf9","description":"Who We Are \n Lightning AI is the company behind PyTorch Lightning. Founded in 2019, we build an end-to-end platform for developing, training, and deploying AI systems—designed to take ideas from research to production with less friction.\n Through our merger with Voltage Park, a neocloud and AI Factory, Lightning AI combines developer-first software with cost-efficient, large-scale compute. Teams get the tools they need for experimentation, training, and production inference, with security, observability, and control built in.\n We serve solo researchers, startups, and large enterprises. Lightning AI operates globally with offices in New York City, San Francisco, Seattle, and London, and is backed by Coatue, Index Ventures, Bain Capital Ventures, and Firstminute.\n The Way We Work\n The people who thrive here are builders who move fast, communicate openly, take ownership, and continuously improve themselves, their teams, and our company. Here's what that looks like in practice:\n \n Move with Urgency: We move quickly, make thoughtful decisions, and keep momentum. We value action over perfection and learn by shipping.\n Take Ownership: We own outcomes, not just our individual work. We make decisions that move the company forward and follow through.\n Communicate Openly: We communicate directly, seek to understand, and create clarity for others. Honest conversations help us move faster together.\n Build Great Teams: We lead by example, empower others, and create healthy teams where people can do their best work.\n Raise the Bar: We're always improving ourselves. We learn from feedback, consistently challenge ourselves to grow, and focus on the work that matters most.\n Think Long-Term: We design for what's next. We create scalable systems, simplify complexity, and use AI and automation to amplify our impact.\n \n  \n What We're Looking For\n We're are looking for an experienced Senior Research Engineer who has built, trained, and optimized modern transformer-based language models to join our Research Engineering function at Lightning.\n This role will focus on advancing how large language models are trained, fine-tuned, evaluated, and deployed across Lightning AI's platform and real-world customer workloads. It will work across model training, post-training, PyTorch, distributed systems, and AI systems engineering to improve model quality, training efficiency, and developer productivity while collaborating closely with researchers, infrastructure engineers, and customers.\n We're looking for someone who enjoys turning cutting-edge research into production systems. You have deep experience training and improving transformer-based language models, strong software engineering fundamentals, and a passion for solving difficult problems across model training, evaluation, and AI systems. Rather than building applications on top of existing models, you're motivated by improving the models themselves and the systems that power them. Our work spans models that power the Lightning AI platform, customer-specific model workloads, and research that translates into reusable training and platform capabilities.\n This role is hybrid with a minimum of 2 in-office days per week in San Francisco, Seattle, NYC, or London, with fully remote work considered for candidates outside of our office hub locations. All employees participate in occasional team and company offsites. \n  \n What You'll Do \n \n Design, build, and optimize training and post-training pipelines for large language models.\n Improve model quality through supervised fine-tuning, continued pretraining, preference optimization, reinforcement learning, evaluation, and experimentation.\n Build and improve PyTorch-based training infrastructure, tooling, and developer workflows.\n Optimize distributed training across multi-GPU environments by improving throughput, memory efficiency, scalability, and GPU utilization.\n Investigate model training issues, including convergence, instability, communication overhead, and performance bottlenecks.\n Design evaluation methodologies, benchmark models, analyze failure modes, and acheive model improvements through experimentation.\n Collaborate directly with customers to understand real-world workloads and translate those learnings into improvements across Lightning AI's research platform.\n Partner closely with research, infrastructure, and platform engineering teams to build production-ready AI systems.\n Contribute to open-source projects through new features, tooling improvements, documentation, and community engagement\n \n  \n What You’ll Need \n Required Qualifications \n \n Significant experience training, fine-tuning, evaluating, and/or optimizing transformer-based language models using PyTorch.\n Experience with modern LLM training and post-training techniques such as continued pretraining, SFT, RLHF, preference optimization (DPO, PPO, GRPO), reward modeling, or similar approaches.\n Strong understanding of distributed training and multi-node systems, ","salary_min":165000,"salary_max":310000,"location":"New York, NY","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["pytorch","fine-tuning","pre-training","search","gpu","reinforcement-learning","distributed-systems","llm"],"apply_url":"https://job-boards.greenhouse.io/lightningai/jobs/7860628003","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-12T19:54:51Z","expires_at":"2026-11-13T16:55:32.820036Z","created_at":"2026-08-25T18:27:03.622997Z","updated_at":"2026-10-14T16:55:32.823616Z","company_name":"Lightning AI","company_slug":"lightning-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=lightning.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/c329b188-7612-4641-ac6d-796f7502eb3d"},{"id":"783b7feb-6610-43ee-9559-b9c3f01b699b","company_id":"c0136eba-1fff-477a-8968-c5435a645cd3","title":"Technical Lead, Multimodal Transformers","slug":"technical-lead-multimodal-transformers-9e9f79ce","description":"Kodiak Robotics, Inc. was founded in 2018 and has become a leader in autonomous ground transportation committed to a safer and more efficient future for all. The company has developed an artificial intelligence (AI) powered technology stack purpose-built for commercial trucking and the public sector. The company delivers freight daily for its customers across the southern United States using its autonomous technology. In 2024, Kodiak became the first known company to publicly announce delivering a driverless semi-truck to a customer. Kodiak is also leveraging its commercial self-driving software to develop, test and deploy autonomous capabilities for the U.S. Department of Defense.\n Kodiak's autonomy stack is built on AI that fuses diverse sensor streams into a unified, actionable understanding of the world. We are developing GigaFusionNet, a large-scale multimodal transformer that learns rich, joint representations across camera, LiDAR, and radar through attention-based fusion. We are looking for a technical leader to own the architecture direction of this effort and grow the engineers building it. This is a senior individual contributor role with significant scope. You will set technical direction for multimodal fusion at Kodiak, lead the workstream executing against it, and be accountable for the results landing on trucks. In this role, you will: \n \n Own the architecture roadmap for multimodal transformers that fuse camera, LiDAR, and radar into unified representations \n Lead the project end to end: problem framing, experiment design, implementation, and production deployment \n Drive research direction on cross-modal attention, token fusion strategies, and efficient multi-stream tokenization, and make the calls on what gets built \n Set the technical bar through design reviews, code reviews, and architectural decisions on scalable training pipelines \n Mentor junior and mid-level engineers, and raise the level of research execution across the org \n Define the pretraining strategy, including self-supervised and contrastive objectives that learn transferable multimodal representations \n Partner with perception, planning, and infrastructure leads to align model design with system-level latency and compute budgets \n \n What you’ll bring: \n \n PhD with 5+ years of industry experience, or MS/BS with 8+ years, in AI, Computer Science, or a related field \n Track record of leading multi-engineer technical efforts from research through production \n Deep expertise in transformer architectures, particularly in multimodal or multi-stream settings \n Strong command of cross-attention, token fusion, and modality alignment techniques \n Experience mentoring engineers and growing technical talent \n Expert proficiency in Python and PyTorch, including large-scale distributed training and mixed-precision optimization \n Ability to influence technical direction across teams without formal authority \n Passion for building AI that reasons over the full breadth of sensory input to operate safely in the real world \n \n What we offer: \n \n Competitive compensation package including equity and annual bonuses \n Excellent Medical, Dental, and Vision plans through Kaiser Permanente, Cigna, and  MetLife (including a medical plan with infertility benefits) \n MetLife Legal Services, Identity \u0026 Fraud Protection, Hospital Indemnity Insurance, Accident Insurance, \u0026 Critical Illness Insurance \n Flexible PTO, 10 paid holidays, and generous parental leave policies \n Our office is centrally located in Mountain View, CA \n Office perks: dog-friendly, free catered lunch, a fully stocked kitchen, and free EV charging \n Long Term Disability, Short Term Disability, Life Insurance \n Wellbeing Benefits - Headspace through Cigna, Calm through Kaiser, One Medical, Gympass, Spring Health through Cigna, Rula (mental health navigation)  \n Fidelity 401(k) \n Commuter, FSA, Dependent Care FSA, HSA \n Various incentive programs (referral bonuses, patent bonuses, etc.) \n The pay range listed below reflects the base salary  in our SF/Silicon Valley location,  across several internal levels. Actual starting pay will be based on job-related factors including: work location, experience, relevant training, education, skill level and performance during interview. Total compensation at Kodiak includes base pay, equity, bonus and a competitive benefits package\n California Pay Range\n $230,000 — $300,000 USD \n  \n At Kodiak, we strive to build a diverse community working towards our common company goals in a safe and collaborative environment where harassment of any kind is strictly prohibited. Kodiak is committed to equal opportunity employment regardless of race, ethnicity, religion, gender identity, sexual orientation, age, disability, or veteran status, or any other basis protected by applicable law.\n  \n In alignment with its business operations, Kodiak adheres to all relevant statutes, regulations, and administrative prerequisites. Accordingly, roles that carry more ","salary_min":230000,"salary_max":300000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["distributed-systems","pre-training","pytorch","autonomous-vehicles","robotics","data-pipeline","transformers","machine-learning"],"apply_url":"https://job-boards.greenhouse.io/kodiak/jobs/4363440009","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-11T16:42:20Z","expires_at":"2026-11-13T16:57:47.374594Z","created_at":"2026-08-25T18:28:50.595861Z","updated_at":"2026-10-14T16:57:47.377827Z","company_name":"Kodiak Robotics","company_slug":"kodiak-robotics","company_logo_url":"https://www.google.com/s2/favicons?domain=kodiak.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/783b7feb-6610-43ee-9559-b9c3f01b699b"},{"id":"991f8296-af04-49ea-b15e-7d9b5fc51b29","company_id":"a0000000-0000-0000-0000-000000000001","title":"AI Infrastructure Operations, Demand Planning","slug":"ai-infrastructure-operations-demand-planning-3cf6fb61","description":"About Anthropic \n Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.\n About the Role\n Anthropic runs one of the largest and fastest-growing infrastructure fleets in the industry, across multiple accelerator families, CPU families, clouds, neoclouds, and on-prem sites. Capacity Engineering owns the data, tooling, and systems that let Anthropic plan, measure, and maximize utilization of that fleet: we partner on supply deals, wire telemetry from day zero, own the canonical capacity data layer, and build the planning and enforcement tools every research and product team relies on. This role sits in the Planning pillar, on the Demand Planning team, and works daily with research engineering, pretraining, inference, compute supply, finance, and external vendors.\n You own the tranches. The job has two halves that feed each other. Upstream, you take the Demand Planning forecast and turn it into per-tranche requirements — shape, interconnect, region, supporting resources, date — and carry those into sourcing negotiations and data center build reviews so we contract for capacity we can actually use when we need it. Downstream, you own the integrated schedule and system of record for every tranche in flight — from contracted through reserved, ingested, in-cluster, healthy, and occupied — and you drive the owners of each hop to their dates. Every slip you see downstream becomes a contract-language fix, an automation, or a correction fed back to the forecast.\n What you'll do\n \n Turn the forecast into per-tranche requirements. Take the Demand Planning forecast plus direct input from research, pretraining, and inference planners, and convert it into concrete accelerator, interconnect, region, supporting-resource, and date requirements for each tranche. Represent those in sourcing negotiations and data center build reviews, including which contractual terms actually move delivery dates.\n Qualify tranches for deliverability before signature. The Capacity Planner signs fit-to-forecast; you sign whether the shape can land schedulable, healthy, and instrumented in that region on that date, with storage, egress, identity in place.\n Close the delivery loop. Track forecast-versus-delivered on shape, region, and timing for every tranche; publish the variance; and feed it back to Demand Planning and into the next contract.\n Own the bring-up system of record. Define the canonical contract-to-occupied state machine with explicit entry and exit criteria per stage, and make it a first-class object in the capacity data layer so every downstream tool sees in-flight capacity, not only what has landed.\n Run a portfolio of bring-ups in parallel — new cloud regions, on-prem sites, neocloud blocks — with one integrated schedule spanning provider milestones, cluster creation, network turn-up, storage readiness, health burn-in, and first-workload landing. \n Drive readiness automation: All capacity systems are fully integrated for all new capacity, from contracted through ingested, automated and scaled.\n Instrument and publish the numbers that matter — time-to-occupied and paid-idle dollars per tranche — with executive-level reporting on status, tradeoffs, and risk across the portfolio.\n \n What you bring\n \n Significant experience delivering large-scale infrastructure — cloud regions, accelerator clusters, HPC systems, or bare-metal fleets — at multi-region scale or ≥10k accelerators (or CPU/storage equivalent).\n Technical range from through cluster orchestration and node health, up to the telemetry and planning tables on top — enough to debug where they disagree rather than route it.\n SQL and enough Python to answer your own questions and build your own reporting.\n A degree in a technical field or an equivalent engineering track record.\n \n Preferred\n \n Reserved-capacity onboarding, private offers, or capacity commitments with cloud or neocloud providers.\n Enough demand-planning exposure to challenge a forecast, translate it into per-tranche requirements, and feed delivery variance back into it.\n Data center or colocation delivery: power and space planning, network turn-up, site acceptance, vendor management.\n Accelerator health and burn-in, collective-communications sanity testing, or fleet-health SLOs — and a rigorous definition of \"healthy.\"\n Systems of record or lifecycle services for infrastructure assets.\n Onboarding a new hardware generation into an existing scheduler and observability stack.\n The annual compensation range for this role is listed below. \n For sales roles, the range provided is the role’s On Target Earnings (\"OTE\") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for","salary_min":320000,"salary_max":405000,"location":"San Francisco, CA","workplace":"hybrid","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["pre-training","search","alignment","infrastructure"],"apply_url":"https://job-boards.greenhouse.io/anthropic/jobs/5382750008","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-11T12:14:01Z","expires_at":"2026-11-13T16:54:54.991482Z","created_at":"2026-08-25T18:26:10.627699Z","updated_at":"2026-10-14T16:54:54.994273Z","company_name":"Anthropic","company_slug":"anthropic","company_logo_url":"https://www.google.com/s2/favicons?domain=anthropic.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/991f8296-af04-49ea-b15e-7d9b5fc51b29"},{"id":"0629b5fc-5e11-4813-932b-9d15bb730500","company_id":"d49c7f16-1314-459a-acab-7b3d38ee01a9","title":"Member of Technical Staff, Security Engineer","slug":"member-of-technical-staff-security-engineer-5c946307","description":"Magic’s mission is to build safe AGI that accelerates humanity’s progress on the world’s most important problems. We believe the most promising path to safe AGI lies in automating research and code generation to improve models and solve alignment more reliably than humans can alone. Our approach combines frontier-scale pre-training, domain-specific RL, ultra-long context, and inference-time compute to achieve this goal.\n\n\n\n\nABOUT THE ROLE:\n\nAs a Security Engineer at Magic, you will build the software, automation, and infrastructure that protect our models, systems, and engineering organization from both traditional and frontier AI cyber threats. You will respond to security threats, help us build good practices around security incident/threat response.\n\nThe role sits at the intersection of software engineering, infrastructure, and security. You will work closely with researchers, infrastructure engineers, and platform teams to reduce risk while considering the productivity impact. You will help us make the secure path the easy, productive path.\n\nThe threat landscape is changing, supply chain attacks are a major concern, prompt injections and other LLM-native attack vectors are worrying. As frontier AI systems become increasingly capable, security is evolving beyond defending infrastructure alone. Modern models can autonomously discover vulnerabilities, chain exploits across multiple systems, manipulate agents through prompt injection, and escape “secure” containment.\n\nAI safety is extremely important to us at Magic. You will collaborate with our alignment team on whatever they might need to ensure that Magic's models are a safe net-benefit to the world.\n\n\n\n\nWHAT YOU MIGHT WORK ON: \n\n - Design, build, and maintain security tooling and automation used by Magic's engineering teams\n\n - Secure developer infrastructure, CI/CD pipelines, build systems, and production services and make the platform tools easy to use\n\n - Support our sandboxes team with building secure sandboxing and isolation for untrusted code, agent and model execution\n\n - Build guardrails against emerging AI attack vectors, including prompt injection, tool abuse, exploit chaining, and autonomous model behavior\n\n - Protect model weights, research artifacts, datasets, and production infrastructure from unauthorized access, lateral movement, and exfiltration\n\n - Respond to security incidents and live threats\n   \n   \n\n\nWHAT WE’RE LOOKING FOR: \n\n - Software engineering skills with experience building production systems in Rust, Go, Python, C++, Kotlin, TypeScript, or similar languages\n\n - A very good understanding of modern MITRE att\u0026ck techniques\n\n - Actively engaged in the role of LLMs in securing and attacking software systems\n\n - On-call readiness 24/7, assisted by our team\n\n - Experience securing distributed systems, cloud infrastructure, or large-scale production environments\n\n - Ability to develop high-complexity cloud Linux-based exploits\n\n - Deep understanding of UNIX or other operating systems, networking, authentication, authorization, and modern security architecture\n\n - Experience building automation and tooling to secure fast paced organizations\n\n - Track record of exceptional personal integrity, accountability and trustworthiness in high autonomy environments\n\n\n\n\nOUR CULTURE\n\n - Integrity. Words and actions should be aligned\n\n - Hands-on. At Magic, everyone is building\n\n - Teamwork. We move as one team, not N individuals\n\n - Focus. Safely deploy AGI. Everything else is noise\n\n - Quality. Magic should feel like magic\n\nMagic strives to be the place where high-potential individuals can do their best work. We value quick learning and grit just as much as skill and experience.\n\n\n\n\nCOMPENSATION, BENEFITS, AND PERKS (US):\n\n - Annual salary ranges between $225K - $550K based on experience\n\n - Equity is a significant part of total compensation, in addition to salary\n\n - 401(k) plan with 6% salary matching\n\n - Generous health, dental and vision insurance for you and your dependents\n\n - Unlimited paid time off\n\n - Visa sponsorship and relocation stipend to bring you to SF, if possible\n\n - A small, fast-paced, highly focused team","salary_min":225000,"salary_max":550000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["llm","alignment","code-generation","cloud","security","distributed-systems","pre-training"],"apply_url":"https://jobs.ashbyhq.com/magic.dev/f9b3e872-cffa-400a-b9e0-621149c5f566/application","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-07T02:15:51.453Z","expires_at":"2026-11-13T16:56:06.933194Z","created_at":"2026-08-25T18:27:38.225391Z","updated_at":"2026-10-14T16:56:06.93616Z","company_name":"Magic","company_slug":"magic","company_logo_url":"https://www.google.com/s2/favicons?domain=magic.dev\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/0629b5fc-5e11-4813-932b-9d15bb730500"},{"id":"0c6dbcba-9806-47c5-96d9-c315911a45b7","company_id":"e8dfc4ee-9649-4fd0-9c16-90d38a1954e1","title":"Member of Technical Staff, Lead Researcher","slug":"member-of-technical-staff-lead-researcher-43af18fd","description":"About the Role \n DoorDash is building an AI Research org from the ground up, and we're hiring our founding researchers. This is not a role inside an existing team — it's a role that defines what the team becomes. You'll have an outsized influence on the research agenda, hiring, culture, infrastructure choices, and how research connects to the rest of DoorDash.\n DoorDash sits on a uniquely valuable substrate for AI research: a real-world, multi-sided marketplace operating at massive scale, with millions of consumers, merchants, and Dashers generating data that no academic lab and few companies can access. We want to build a research org that takes that seriously — one that produces work the broader field cares about, and that fundamentally reshapes how local commerce works.\n You should apply if you want to do ambitious, publishable research in an environment with the data, compute, and operational reach to actually deploy what you build.\n What You'll Do \n \n Set the research agenda for one or more areas of DoorDash AI Research, in close collaboration with the founding team and leadership\n Lead high-impact research projects end-to-end — from problem framing through publication and, where appropriate, production deployment\n Help build the team — interview, recruit, and mentor researchers, engineers, and fellows joining the org\n Shape the org's culture and operating model — how we publish, how we collaborate with product teams, how we balance open research with proprietary work\n Partner across DoorDash with ML platform, product, and operations teams to identify the highest-leverage research bets and translate findings into real-world impact\n \n What You'll Have Access To \n \n Novel proprietary data at marketplace scale — logistics traces, merchant operations, consumer behavior, real-time supply and demand signals, and longitudinal data unavailable anywhere else\n Scalable data collection — ability to design and run structured data collection, leveraging DoorDash’s world-class operational scale, from in-the-wild image and video capture to operational task demonstrations and human-in-the-loop annotation, at a scale and physical-world coverage no other org can match\n High compute budgets for training and inference, sized to support frontier-scale experimentation including large-model pre-training and post-training, RL training runs, and large-scale evaluation sweeps\n Full research infrastructure — DoorDash's internal RL stack, RL environments built on real operational systems, training and evaluation pipelines, and agent evaluation harnesses, with engineering support to extend them as your research demands\n Direct access to leadership — a seat at the table for the decisions that shape the research org, with the autonomy to operate as a principal-level researcher\n Publication freedom — we expect and support publication at top venues (NeurIPS, ICML, ICLR, RSS, CoRL, KDD, etc.) with a fast, supportive internal review process\n Compute and data for external collaborators — budget to bring in academic collaborators, fellows, and visiting researchers as your agenda requires\n \n Research Areas \n We are broadly interested in researchers across the following areas, though the right candidate may reshape this list:\n \n Agentic systems for logistics and local commerce — long-horizon planning, tool use, multi-agent coordination, and evaluation methodologies for agents operating in physical-world marketplaces\n Memory and personalization — transfer RL, continual learning, harness-based improvements, and systems that adapt to individual consumers, merchants, and Dashers over time without catastrophic forgetting or unsafe drift\n Foundation models for marketplace dynamics — forecasting, pricing, matching, and personalization at marketplace scale, including domain-specific pre-training and post-training\n Evaluation and measurement — new benchmarks, eval harnesses, and methodologies for ML systems deployed in messy, real-world operational settings\n Multimodal understanding — vision, speech, and language applied to merchant catalogs, in-store and on-the-road imagery, and consumer interfaces\n Robotics and embodied AI for last-mile delivery — perception, planning, and learning systems for the physical edge of the marketplace\n \n Who We're Looking For \n \n A strong research track record:  first-author publications at top ML venues, or equivalent demonstrated output (widely-used systems, influential open-source work, or research artifacts adopted at scale)\n Experience leading ambitious research projects end-to-end, from problem framing through to results that other people built on\n Taste:  the ability to identify which problems are worth working on, when an approach is exhausted, and when a result is real\n A builder's instinct:  comfortable working close to real systems and real data, not just on benchmarks\n Excitement about the founding-team aspects of the role: hiring, agenda-setting, culture-building, and","salary_min":203500,"salary_max":299300,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["agents","search","cloud","robotics","pre-training","healthcare","fine-tuning","generative-ai"],"apply_url":"https://job-boards.greenhouse.io/doordashusa/jobs/8105902","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-04T16:00:31Z","expires_at":"2026-11-13T17:03:28.665154Z","created_at":"2026-08-25T18:33:45.011426Z","updated_at":"2026-10-14T17:03:28.668087Z","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/0c6dbcba-9806-47c5-96d9-c315911a45b7"},{"id":"2229113f-ed51-4e63-bb05-2344fae3bf5c","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Staff Research Scientist, Foundation Models Recipes","slug":"staff-research-scientist-foundation-models-recipes-4b8515df","description":"Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n The mission of the Waymo AI Foundations team is to develop machine learning solutions addressing open problems in autonomous driving, towards the goal of safely operating Waymo vehicles in dozens of cities and under all driving conditions. As part of our work, we also initiate and foster collaborations with other research teams in Alphabet. AI Foundations areas that we are currently focusing on include reinforcement learning, learning from demonstration, generative modeling, Bayesian inference, hierarchical learning, and robust evaluation.\n In this hybrid role, you will report to a Senior Director of AI Foundations \n You will: \n \n Own the data recipe for Waymo’s Foundation Model pre-training and post-training\n Lead and drive science on best practices around clustering, filtering, de-duplication, long-tail data mining, memorization, etc. \n Tech-lead a team of research engineers to build the data flywheel to enable the above from Waymo’s massive driving data\n Integrate emerging research from the broader community to do rigorous ablations and promising data research techniques. This includes scaling ladders for pre-training, data mix optimizations and RL recipes (preferences) for post-training. \n Engage with the wider research community on best practices around data centric evaluation creation and refinement \n Partner with engineering and research teams across Waymo to share recipes, techniques, and post-training best practices to accelerate our collective know-how\n \n You have: \n \n PhD or Masters in Computer Science, Machine Learning, Robotics, or a similar technical field; with 3+ years of industry or post-doc research experience in Data-centric AI, Reinforcement Learning or Foundation Models\n Demonstration of original contributions to the field through high-impact publications (ArXiv, peer-reviewed conferences like NeurIPS/ICLR/CVPR), technical blog posts, or significant open-source contributions\n Proficiency and in-depth knowledge of the inner workings of an ML framework (e.g. Pytorch, JAX, Tensorflow)\n Deep learning experience, especially with generative models, e.g., LLMs/VLMs, and/or reinforcement learning\n \n We prefer: \n \n Extensive experience working with data and recipes for large scale foundation models\n ML infra experience: training, evaluating and deploying ML models at scale\n \n In accordance with Washington state law, we are highlighting our comprehensive benefits package, which is available to all eligible US based employees. Benefits for this role include:\n \n Health, dental, vision, life, disability insurance\n Retirement Benefits: 401(k) with company match\n Paid Time Off: 20 days of vacation per year, accruing at a rate of 6.15 hours per pay period for the first five years of employment\n Sick Time: 40 hours/year (statutory, where applicable); 5 days/event (discretionary)\n Maternity Leave (Short-Term Disability + Baby Bonding): 28-30 weeks\n Baby Bonding Leave: 18 weeks\n Holidays: 13 paid days per year\n \n Please note that Waymo may not be able to employ remotely in all locations. Please speak with your recruiter about your preferred location for remote work when you begin the interview process\n The expected base salary range for this full-time position across US locations is listed below. Actual starting pay will be based on job-related factors, including exact work location, experience, relevant training and education, and skill level. Your recruiter can share more about the specific salary range for the role location or, if the role can be performed remote, the specific salary range for your preferred location, during the hiring process.  \n Waymo employees are also eligible to participate in Waymo’s discretionary annual bonus program, equity incentive plan, and generous Company benefits program, subject to eligibility requirements.  \n Salary Range\n $251,000 — $310,000 USD","salary_min":251000,"salary_max":310000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["deep-learning","pre-training","tensorflow","robotics","reinforcement-learning","autonomous-vehicles","llm","search"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=8075587","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-08-03T23:19:22Z","expires_at":"2026-11-13T16:55:55.251617Z","created_at":"2026-08-25T18:27:26.009368Z","updated_at":"2026-10-14T16:55:55.254927Z","company_name":"Waymo","company_slug":"waymo","company_logo_url":"https://www.google.com/s2/favicons?domain=waymo.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/2229113f-ed51-4e63-bb05-2344fae3bf5c"},{"id":"426da38a-255b-4b18-aa49-edd706e59a31","company_id":"6ea0f41a-b13e-481a-b410-5195f391f939","title":"Research Engineer, Large-Scale Training","slug":"research-engineer-large-scale-training-19a25c49","description":"About the Role \n The Model Shaping team at Together AI works on products and research for tailoring open foundation models to downstream applications. We build services that allow machine learning developers to choose the best models for their tasks and further improve these models using domain-specific data. In addition, we develop new methods for more efficient model training and evaluation, drawing inspiration from a broad spectrum of ideas across machine learning, natural language processing, and ML systems. \n As a Research Engineer on the Scaling Team within Model Shaping, you will turn cutting-edge research on efficient foundation model training into robust, high-performance systems. You will profile and optimize Together's training infrastructure, identify performance bottlenecks across the stack, and implement state-of-the-art techniques from both the research literature and our own scientists in production environments. \n Your work will directly shape the fine-tuning experience of Together's customers. You will rapidly bring newly released open-source models onto the Model Shaping platform, ensuring they train efficiently and reliably across diverse customer workloads. Working closely with Research Scientists, you will also build the experimental infrastructure that accelerates research and enables validated ideas to be deployed reliably at scale. \n Responsibilities \n \n Design, implement, and optimize core components of Together's large-scale training infrastructure. \n Integrate new model architectures, validate training correctness and convergence, and optimize performance for production fine-tuning workloads. \n Profile distributed training workloads to identify and eliminate bottlenecks across compute, memory, and communication. \n Design and execute experiments to validate performance hypotheses and benchmark new approaches against state-of-the-art methods. \n Partner closely with Research Scientists to productionize novel training methods and contribute to publications and open-source releases. \n Rapidly enable support for newly released open-source foundation models on the Together platform. \n Build and maintain experimental infrastructure that accelerates research while ensuring production-quality reliability and scalability. \n \n Requirements \n \n Demonstrated ability to independently take ambiguous performance or infrastructure problems from investigation through deployment. \n Strong programming skills in Python and PyTorch, with an emphasis on writing efficient, maintainable code. \n Hands-on experience training or fine-tuning large neural networks in multi-GPU or multi-node environments. \n Solid understanding of ML systems fundamentals, including GPU architecture, mixed-precision training, and distributed training paradigms such as data, tensor, pipeline, or expert parallelism. \n Strong communication skills and the ability to collaborate effectively with both researchers and engineers. \n Passion for staying current with advances in AI research and applying them to real-world systems. \n Excitement about translating cutting-edge research into production systems that deliver customer impact. \n \n Nice to Have \n \n Experience writing optimized NVIDIA GPU kernels using CUDA or Triton, or implementing communication collectives with technologies such as NCCL or NVSHMEM. \n Experience with large-scale training frameworks such as FSDP, DeepSpeed, Megatron-LM, or custom distributed training systems. \n Experience optimizing distributed training for compute efficiency, memory efficiency, or scalability. \n Experience running and managing large-scale GPU experiments, including scheduling, monitoring, and fault tolerance. \n Contributions to widely used open-source ML or ML systems projects. \n Experience building or operating ML products or managed services used by external customers. \n \n About Together AI \n Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month.\n Compensation \n We offer competitive compensation, startup equity, health insurance, and other benefits. The US base salary range for this full-time position is $200,000 - $290,000. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge. \n Equal Opportunity \n Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status","salary_min":200000,"salary_max":290000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["nlp","fine-tuning","generative-ai","pytorch","reinforcement-learning","pre-training","gpu","search"],"apply_url":"https://job-boards.greenhouse.io/togetherai/jobs/5199554007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-30T16:52:05Z","expires_at":"2026-11-13T16:55:08.845326Z","created_at":"2026-07-31T14:02:19.892066Z","updated_at":"2026-10-14T16:55:08.84845Z","company_name":"Together AI","company_slug":"together-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=together.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/426da38a-255b-4b18-aa49-edd706e59a31"},{"id":"a265241a-37c6-4e16-9467-260c02fa0b0b","company_id":"6ea0f41a-b13e-481a-b410-5195f391f939","title":"Senior Software Engineer, Customer Insights","slug":"software-engineer-customer-insights-f7870102","description":"About the Role \n Together.ai is looking for a Senior Software Engineer to join the Customer Insights team. Customer Insights owns the customer-facing visibility layer of Together's Cloud — the systems that help customers understand what's happening in their environment, trace back what happened and why, and act on it with confidence.\n Every time a customer needs to understand their own activity — what happened, who did it, when, and whether it needs attention — they rely on the systems we build. Whether it's a developer inspecting recent activity, an enterprise team auditing their organization, or an operator diagnosing an anomaly, we make that visibility clear and trustworthy: simple for everyday cases, robust enough for complex organizational needs. We're turning today's fragmented visibility patterns into coherent product and platform foundations, and building toward a next generation of insight tooling that summarizes activity, explains anomalies, and correlates signals across surfaces — for both human operators and autonomous agents.\n You'll take ambiguous problems and turn them into a clear technical direction. You'll set the technical approach for significant pieces of the system, weigh in on architecture beyond your immediate area, and help less experienced engineers navigate unfamiliar problem spaces. You'll have real autonomy over how problems get solved, paired with a team that reviews, debates, and learns in the open.\n Responsibilities \n \n Design and ship core features of Together's Customer Insights platform — the systems customers rely on to see, trace, and act on their own activity.\n Drive the technical design for new capabilities, including proposing architecture, writing design docs, and getting buy-in across teams.\n Partner with Data Platform and Observability engineering teams to leverage Together’s core data stack and capabilities to build out Customers Insights data collection and stream processing systems.\n Own significant areas of the system end-to-end — from design through implementation, testing, rollout, and long-term health.\n Write and maintain critical-path backend and product code used by multiple teams and customer-facing surfaces. Identify risks and blockers early, drive resolution across team boundaries.\n \n Requirements \n \n 4+ years building and operating production distributed systems or customer-facing backend platforms.\n Experience designing and scaling data pipelines for high-volume ingestion and real-time querying specifically (the \"at scale\" specialization, distinct from #1's general systems experience).\n Strong programming skills in one or more of Go, Python, Java,TypeScript, C++, or similar production languages.\n Excellent communication skills – able to write clear design docs and work effectively with both technical and non-technical team members\n Strong data modeling instincts — comfort working with schemas, queries, and relational and/or non-relational data at scale.\n Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related technical field, or equivalent practical experience.\n \n  \n Nice to Have \n \n Interest in, or some exposure to, stream processing, data pipelines, notification systems, or analytical systems (this is a great team to learn them on) \n \n About Together AI \n Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month.\n Compensation \n We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $180k - 250k + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge. \n Equal Opportunity \n Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more. \n Please see our privacy policy at https://www.together.ai/privacy .","salary_min":180000,"salary_max":250000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["pre-training","reinforcement-learning","agents","fine-tuning","distributed-systems","data-pipeline"],"apply_url":"https://job-boards.greenhouse.io/togetherai/jobs/5196805007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-30T16:31:23Z","expires_at":"2026-11-13T16:55:08.920651Z","created_at":"2026-07-31T14:02:21.18758Z","updated_at":"2026-10-14T16:55:08.923337Z","company_name":"Together AI","company_slug":"together-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=together.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/a265241a-37c6-4e16-9467-260c02fa0b0b"},{"id":"ff5fbb82-779d-4df0-a414-4a23ff8397cb","company_id":"6ea0f41a-b13e-481a-b410-5195f391f939","title":"Senior Product Engineer, Fullstack","slug":"senior-product-engineer-fullstack-9bbf621c","description":"About the Role \n Together AI's product is what developers touch every day — Playground, Model Garden, Fine-Tuning, File Management, Batch Processing, Model Evals. These surfaces are how users experience the platform, and they need to be fast, intuitive, and something developers genuinely love using. That's what this role is about.\n We're growing the Product Engineering team and looking for a fullstack engineer who's motivated by shipping things users genuinely value — not features that just look good in a slide deck. You'll plug directly into new and existing workstreams and start shipping quickly, working across our core product surfaces to improve existing capabilities and build new ones.\n This is a high-ownership role on a small, fast-moving team, where one strong engineer moves the needle visibly. You'll see your work in production, instrument and measure it, and use that to shape what comes next.\n Responsibilities \n \n Ship improvements and new capabilities across Together AI's core product surfaces — Playground, Model Garden, Fine-Tuning, File Management, Batch Processing, and Model Evals\n Own projects and features end-to-end, from data model and API integration through the user interface\n Collaborate closely with other engineers to build clean, typed, reusable interfaces in our web application\n Instrument and measure what you ship — add product analytics, monitor system health, and prove users are getting value with data\n Work across design, product, and engineering to ship things that unlock user value\n Raise the bar on frontend and fullstack quality across the team through code review and collaborative architecture decisions\n \n Requirements \n \n 4–7 years of experience building and shipping real product to real users\n Strong JavaScript/TypeScript fundamentals; TypeScript experience highly valued\n Strong React expertise in a large-scale, production environment\n Full-stack fluency: you connect the dots from data model to API design to state management to the UI, and know how to make the right call at each layer in service of clean, maintainable code and an intuitive user experience\n A pragmatic approach to engineering — you know when to move fast and when to slow down, and you make that call based on user value\n Curiosity about AI and LLMs — you want to understand the models and products you're building on top of, not just the code around them\n Adept at building and improving agentic workflows, with good judgment for when to lean on AI and when not to\n Cross-functional \"we ship it\" mentality — no narrow ownership, no finger-pointing\n Next.js, Tailwind, and shadcn/ui experience is a plus\n Familiarity with product analytics (e.g., Amplitude) and systems monitoring (e.g., Grafana) is a plus\n Experience with CI/CD workflows is a plus\n Experience with a backend language like Go or Python\n \n  \n About Together AI \n Together AI, the AI Native Cloud, is purpose-built for AI engineers. AI application developers get high-performance inference that scales reliably, fine-tuning and reinforcement learning for creating frontier-level specialized models, and pre-training at massive scale for fully custom intelligence, all around a marketplace of leading open models that teams can run, adapt, and own. Trusted by Cursor, Decagon, ElevenLabs, Salesforce, and Zoom, Together serves 400+ trillion tokens a month.\n Compensation \n We offer competitive compensation, startup equity, health insurance and other competitive benefits. The US base salary range for this full-time position is: $160,000 - 230,000 + equity + benefits. Our salary ranges are determined by location, level and role. Individual compensation will be determined by experience, skills, and job-related knowledge.\n Equal Opportunity \n Together AI is an Equal Opportunity Employer and is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and more.\n Please see our privacy policy at https://www.together.ai/privacy .","salary_min":160000,"salary_max":230000,"location":"San Francisco, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"senior","tags":["agents","api-design","llm","pre-training","reinforcement-learning","fine-tuning","fullstack"],"apply_url":"https://job-boards.greenhouse.io/togetherai/jobs/5192898007","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-27T21:04:54Z","expires_at":"2026-11-13T16:55:08.902938Z","created_at":"2026-07-28T14:02:13.100596Z","updated_at":"2026-10-14T16:55:08.905508Z","company_name":"Together AI","company_slug":"together-ai","company_logo_url":"https://www.google.com/s2/favicons?domain=together.ai\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/ff5fbb82-779d-4df0-a414-4a23ff8397cb"},{"id":"337551aa-c77a-4af9-bf31-2e1dcf3a2fca","company_id":"6ce2d21e-b00f-4343-9bd0-5ac62ff81431","title":"Staff Machine Learning Engineer - Vision-Language Foundation Models","slug":"staff-machine-learning-engineer-vision-language-foundation-models-17d972cb","description":"Waymo is an autonomous driving technology company with the mission to be the world's most trusted driver. Since its start as the Google Self-Driving Car Project in 2009, Waymo has focused on building the Waymo Driver—The World's Most Experienced Driver™—to improve access to mobility while saving thousands of lives now lost to traffic crashes. The Waymo Driver powers Waymo’s fully autonomous ride-hail service and can also be applied to a range of vehicle platforms and product use cases. The Waymo Driver has provided over ten million rider-only trips, enabled by its experience autonomously driving over 100 million miles on public roads and tens of billions in simulation across 15+ U.S. states.\n The Team \u0026 Mission: In the Oracle Perception team, our mission is to build the ultimate cognitive engine for autonomous driving. We are pioneering the use of large multimodal foundation models (e.g., Gemini) to build a powerful offboard reasoning and data flywheel system. We are moving beyond traditional perception to true scene understanding and driving actions—building offboard models that can comprehend complex driving problems, predict object/scene dynamics, and deduce driving paths with logical rationale.\n Our core focus is advancing the VLM foundation itself. By pushing the boundaries of multimodal pre-training and state-of-the-art post-training (SFT, RL) , we are creating models capable of rich, reasoning-based autolabeling at a massive scale. This closed-loop data engine directly powers the training and evolution of Waymo's real-time onboard models. If you are passionate about defining VLM training recipes, scaling laws, and unlocking complex reasoning via RL, this is your opportunity to redefine the foundation of autonomous driving.\n In this hybrid role, you will report to a Senior Staff Technical Lead Manager.\n You Will: \n \n Drive Pre-training \u0026 Domain Adaptation: Lead the technical strategy for curating and constructing massive-scale, high-quality multimodal pre-training datasets. Define data mixture strategies to instill deep, Waymo-specific driving intuition and physics-grounded understanding into foundation models without catastrophic forgetting.\n Lead Post-Training \u0026 Reasoning Enhancement: Design and implement state-of-the-art fine-tuning (SFT) and Reinforcement Learning (RLHF/RLAIF, DPO/GRPO/PPO) pipelines. Drastically improve the model’s instruction-following and complex reasoning capabilities (e.g., Chain-of-Thought, spatial-temporal reasoning, and driving rationale prediction).\n Pioneer the VLM Data Flywheel: Architect the highly scalable inference and evaluation pipelines that leverage these trained Gemini-class models to autonomously source, sample, and autolabel critical edge cases, directly accelerating the onboard perception models.\n Define Training Recipes \u0026 Scaling Laws: Conduct rigorous ablation studies to optimize model architectures, token budgets, and loss functions. Establish best practices for scaling multimodal training efficiently on large GPU/TPU clusters.\n Drive Cross-Functional AI Strategy: Act as the principal technical visionary across ML Infra, Perception, Behavior, and AI Foundation teams. Drive consensus on the data flywheel architecture and embed VLM reasoning capabilities seamlessly into the broader autonomous vehicle stack.\n Provide Staff-Level Technical Leadership: Own the long-term technical roadmap for foundation model development. Mentor senior engineers, lead rigorous design reviews, and establish standard-setting engineering practices from advanced prototyping to production deployment.\n \n You Have: \n \n Master’s degree in Computer Science, AI, ML, or a related technical field.\n 8+ years of hands-on experience designing, training, and scaling deep learning models, with at least 3+ years focused deeply on training Large Language Models (LLMs) or Vision-Language Models (VLMs) .\n Proven expertise in the full lifecycle of Foundation Models: from pre-training data curation (interleaved formats, tokenization) and distributed training to advanced post-training techniques.\n Expert-level understanding of training infrastructure and distributed paradigms (e.g., FSDP, Megatron, JAX/Pax) required for training massive models reliably.\n Expert-level software engineering fundamentals using Python, PyTorch, or JAX, with a track record of building reliable, highly scalable ML systems.\n Proven ability to operate with high ambiguity, define technical roadmaps, and drive complex, multi-quarter technical initiatives across multiple teams in a fast-paced environment.\n \n We Prefer: \n \n PhD in Computer Science, Artificial Intelligence, or a related field.\n Strong publication record in top-tier AI venues (e.g., NeurIPS, ICML, ICLR, CVPR) focusing on foundation models, large-scale training, reinforcement learning, or reasoning.\n Deep experience with advanced Reinforcement Learning paradigms applied to language or vision tasks ( focusing on improving System 2 thinking, logical deduct","salary_min":251000,"salary_max":310000,"location":"Mountain View, CA","workplace":"onsite","remote_scope":"not_remote","job_type":"full-time","experience_level":"lead","tags":["robotics","pytorch","pre-training","deep-learning","reinforcement-learning","distributed-systems","llm","fine-tuning"],"apply_url":"https://careers.withwaymo.com/jobs?gh_jid=8080909","is_featured":false,"is_sticky":false,"status":"active","published_at":"2026-07-23T04:02:40Z","expires_at":"2026-11-13T16:55:55.228796Z","created_at":"2026-07-24T14:04:26.691071Z","updated_at":"2026-10-14T16:55:55.231893Z","company_name":"Waymo","company_slug":"waymo","company_logo_url":"https://www.google.com/s2/favicons?domain=waymo.com\u0026sz=128","quality_score":90,"url":"https://aidevboard.com/job/337551aa-c77a-4af9-bf31-2e1dcf3a2fca"}],"page":1,"per_page":20,"total":186,"total_is_exact":true,"total_pages":10}