{"id":20886,"url":"https://alion.io/job/primeintellect-research-engineer-distributed-training","title":"Research Engineer - Distributed Training","company":{"id":5907,"name":"Prime Intellect","domain":"primeintellect.com","url":"https://alion.io/company/primeintellect","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"A","score":95,"open_postings":30,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":98,"computed_at":"2026-10-16T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":150000,"max":350000,"currency":"USD","period":"year","gross":null,"usd_annual":350000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":true,"relocation_package":false,"has_equity":true,"technologies":[{"name":"Cloudflare","optional":false},{"name":"CUDA","optional":false},{"name":"CUDA Toolkit","optional":false},{"name":"Databricks","optional":false},{"name":"Datadog","optional":false},{"name":"DeepSpeed","optional":false},{"name":"FSDP","optional":false},{"name":"Function Calling","optional":false},{"name":"LangChain","optional":false},{"name":"Megatron-LM","optional":false},{"name":"OpenAI","optional":false},{"name":"OpenRouter","optional":false},{"name":"Perplexity","optional":false},{"name":"Post-training","optional":false},{"name":"Pre-training","optional":false},{"name":"PyTorch","optional":false},{"name":"Ray","optional":false},{"name":"SFT","optional":false},{"name":"Together AI","optional":false},{"name":"Tool Use","optional":false},{"name":"Triton","optional":false},{"name":"vLLM","optional":false},{"name":"Zapier","optional":false}],"status":"live","first_seen_at":"2026-07-08T18:43:34Z","employer_posted_date":"2026-07-08","last_verified_at":"2026-10-16T12:58:49Z","board_verified":true,"closed_at":null,"days_open":99,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":99},"description":"Own Your Intelligence\nPrime Intellect is building the open superintelligence stack: the infrastructure frontier AI labs build internally, made available to every ambitious AI team.\nOur platform, Lab, unifies compute, environments, evaluations, secure sandboxes, high-performance training, and deployment into one full-stack system for post-training at frontier scale - from SFT and RL to tool use, agent workflows, and continuously improving production models. We are building open frontier AI: open-source models trained end to end for long-horizon tasks like autonomous research, and the full-stack platform our own research team uses to build them. The next generation of AI companies, enterprises, and research teams do not just need more GPUs. They need the ability to turn their own workflows, tools, data, and feedback loops into superintelligence they own.\nWe train open frontier models and ship the same stack to our customers. Its spans the full stack of training, deploying and continuously improving models - compute, large-scale RL, environments, sandboxes, evals, and deployment.\nPrime Intellect has raised $150M in total funding from Founders Fund, Radical Ventures, NVIDIA, and exceptional AI, infrastructure, and enterprise operators - including Andrej Karpathy, Dwarkesh Patel, and leaders and founders from Ramp, Perplexity, Harvey, Mercor, Zapier, Datadog, Semianalysis, Cognition, OpenAI, Thinking Machines, Together AI, SemiAnalysis, LangChain, Browserbase, Cloudflare, Sierra, Databricks, Airbnb, OpenRouter, Standard Intelligence, Fleet, Core Auto, and more. We are looking for people who want to build at the intersection of frontier research, real infrastructure, and go-to-market for a category that does not fully exist yet.\nWhat You’ll Work On\nBuild and optimize the distributed training infrastructure behind our pre-training and large-scale RL training workloads by contributing to our prime-rl framework.\n\nImprove end-to-end training efficiency across compute, memory, networking, and scheduling layers.\n\nDesign and implement low-level performance optimizations, including kernels, communication paths, and runtime improvements.\n\nWork on distributed training systems spanning data, tensor, and pipeline parallel workloads.\n\nHelp shape the architecture of our RL training stack, including async rollout and post-training systems.\n\nContribute to open-source libraries and internal infrastructure used for frontier-scale model training.\n\nCollaborate closely with researchers and infrastructure engineers to translate bottlenecks into concrete systems improvements.\n\nStay at the frontier of training systems, inference systems, compiler/runtime tooling, and hardware-aware optimization techniques.\n\nYou May Be a Fit If You Have\nStrong systems engineering experience in AI/ML infrastructure, especially around large-scale model training or inference.\n\nDeep familiarity with PyTorch and distributed training frameworks such as PyTorch Distributed, DeepSpeed, FSDP, Megatron, vLLM, Ray, or related tooling.\n\nExperience optimizing training performance across kernels, memory movement, communication overhead, or parallelization strategy.\n\nHands-on experience with large-scale training techniques including data parallelism, tensor parallelism, and pipeline parallelism.\n\nStrong understanding of GPU architecture, profiling, and performance debugging.\n\nAbility to identify bottlenecks across the stack and drive improvements from first principles.\n\nComfort working in a fast-moving environment with ambiguous problems and high ownership.\n\nEspecially Exciting\nExperience writing or optimizing CUDA / Triton kernels.\n\nExperience with compiler or runtime optimization for ML systems.\n\nExperience working on RL training infrastructure, rollout systems, or asynchronous training pipelines.\n\nExperience with multi-node GPU clusters and high-performance networking.\n\nContributions to open-source ML systems or infrastructure projects.\n\nInterest in publishing technical work or sharing insights through engineering blogs and technical writing.\n\nBenefits & Perks\nCash Compensation Range of $150-350k, plus equity incentives, aligning your success with the growth and impact of Prime Intellect.\n\nFlexible work arrangements, with the option to work remotely or in-person at our offices in San Francisco.\n\nVisa sponsorship and relocation assistance for international candidates.\n\nQuarterly team off-sites, hackathons, conferences and learning opportunities.\n\nOpportunity to work with a talented, hard-working and mission-driven team, united by a shared passion for leveraging technology to accelerate science and AI.\n\nIf you’re excited about building the systems foundation for frontier-scale training and open superintelligence, we’d love to hear from you.","description_format":"text","description_chars":4768,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Equity","Flexible schedule","Relocation assistance"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":true,"industries":["Reinforcement Learning","Foundation Models","AI Compute & Inference","AI Development 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