{"id":701039,"url":"https://alion.io/job/periodic-ml-systems-engineer","title":"ML Systems Engineer","company":{"id":685855,"name":"Periodic Labs","domain":"periodic.com","url":"https://alion.io/company/periodic","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":85,"open_postings":31,"ghost_share":0.258,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":51,"computed_at":"2026-11-03T05:54:40Z"}},"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":["Menlo Park, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":250000,"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":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"FSDP","optional":false},{"name":"Megatron-LM","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"SGLang","optional":false},{"name":"CUDA","optional":true},{"name":"CUTLASS","optional":true},{"name":"InfiniBand","optional":true},{"name":"LLM","optional":true},{"name":"NCCL","optional":true},{"name":"NVLink","optional":true},{"name":"Ray","optional":true},{"name":"TensorRT","optional":true},{"name":"TensorRT-LLM","optional":true},{"name":"Triton","optional":true},{"name":"vLLM","optional":true}],"status":"live","first_seen_at":"2026-04-29T14:55:07Z","employer_posted_date":"2026-04-29","last_verified_at":"2026-11-03T16:53:28Z","board_verified":true,"closed_at":null,"days_open":188,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":187},"description":"About Periodic Labs\nWe're an AI and physical sciences company building state-of-the-art models to accelerate breakthroughs across materials, energy, and beyond. Backed by world-class investors and growing rapidly, we operate at the pace the frontier requires. Our team brings deep expertise, genuine ownership, and an insatiable drive to push the boundaries of what's scientifically possible.\nAbout the Role\nYou’ll work alongside some of the world’s leading ML systems engineers, including leaders behind Megatron-LM, SGLang, Liger Kernel, TorchRec, CleanRL, TorchRL, and JAX-MD.\nWe’re looking for exceptional ML Systems Engineers to build the agentic infrastructure powering our large-scale training, inference, and reinforcement learning. You’ll own critical pieces of the ML systems stack to maximize performance, scalability, reliability, and productivity for both engineers and AI agents.\nWhat You'll Do\nBuild and optimize large-scale training and reinforcement learning infrastructure while ensuring its correctness\n\nDevelop high-performance inference and serving systems\n\nDesign distributed runtimes and scheduling systems for complex ML workloads\n\nBuild secure and large-scale sandboxing and execution environments\n\nOptimize memory, GPU kernels and communication for maximum throughput and end-to-end efficiency\n\nImprove scalability, reliability, and efficiency across the ML systems stack\n\nWhat We're Looking For\nStrong systems programming and performance engineering skills\n\nExperience building high-performance ML infrastructure at scale\n\nAbility to own complex technical problems end-to-end\n\nStrong coding ability and engineering judgment, including the ability to work effectively with AI agents to design, implement, test, and debug complex systems\n\nHigh ownership, fast execution, and a passion for pushing the frontier of AI systems and accelerating scientific discovery\n\nYou should have deep expertise in at least one of the following:\nTraining: Strong experience building, debugging and optimizing large-scale training systems with Megatron-LM. Familiarity with TorchTitan, FSDP, veRL, Slime, or other distributed training systems is a plus.\n\nDistributed Runtime: Strong experience with Ray. Familiarity with Monarch or other distributed execution frameworks is a plus.\n\nInference: Strong experience with SGLang. Familiarity with vLLM, TensorRT-LLM, or production LLM serving systems is a plus.\n\nSandboxing: Strong experience with secure execution environments, containers, virtualization, or code sandboxing.\n\nGPU Kernels: Strong experience with CUDA, Triton, CUTLASS, CuTe, or custom GPU kernel development.\n\nGPU Communication: Strong experience with NCCL, NVLink, InfiniBand, RDMA, GPUDirect RDMA, or large-scale communication optimization.\n\nMechanics\nMinimum education: Bachelor’s degree or similar experience\nLocation: Menlo Park, CA\nCompensation: $250,000-$350,000 base + equity\nVisa sponsorship: Yes, we sponsor visas.\nWe’re building a team of the world’s best - the scientists, engineers, and problem-solvers who don’t just follow the frontier, they define it. 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