{"id":4026869,"url":"https://alion.io/job/lila-sciences-ml-research-engineer-performance-optimization","title":"ML Research Engineer, Performance Optimization","company":{"id":65163,"name":"Lila Sciences","domain":"lila.ai","url":"https://alion.io/company/lila-ai","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Greenhouse","truth_index":{"grade":"A","score":85,"open_postings":38,"ghost_share":0,"stale_share":0.395,"repost_share":0,"time_to_fill_p50_days":85,"computed_at":"2026-11-10T06:52:35Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Cambridge, United States","San Francisco, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":148000,"max":240000,"currency":"USD","period":"year","gross":null,"usd_annual":240000},"salary_estimate":null,"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"CI/CD","optional":false},{"name":"Function Calling","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Megatron-LM","optional":false},{"name":"Mixture of Experts","optional":false},{"name":"Post-training","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"Tokenization","optional":false},{"name":"vLLM","optional":false},{"name":"CUDA","optional":true},{"name":"Docker","optional":true},{"name":"Flyte","optional":true},{"name":"NCCL","optional":true},{"name":"NVLink","optional":true},{"name":"Ray","optional":true},{"name":"Ray Serve","optional":true},{"name":"Triton","optional":true}],"status":"live","first_seen_at":"2026-11-10T15:37:48Z","employer_posted_date":"2026-11-10","last_verified_at":"2026-11-10T17:15:24Z","board_verified":true,"closed_at":null,"days_open":0,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":0},"description":"Your Impact at LILA\nLila is building a platform where AI and automation co-evolve to accelerate scientific discovery Our AI Research team is looking to hire a ML Research Engineer to maximize iteration speed for mid- and post-training runs on 1T+ parameter LLMs using the latest NVIDIA chips. This involves not only maximizing GPU utilization on an individual training run but also making these optimizations maintainable, reusable, and adaptable so that the rest of the research team always knows how to confidently use the latest optimized stack.\nWhat You'll Be Building\nGPU compute performance: profiling, kernel development, long-context handling, memory footprint optimization\nGPU cluster performance: collective/MoE framework optimization, comm/compute overlap, RDMA/IB/EFA usage, P/D disaggregation\nBig data handling: manage mid-training scale data across storage, streaming, tokenization, and processing\nRL pipeline: optimize the async RL cycle, including environment/sandbox interfacing, long rollout handling, large token metadata (such as router replay), and weight sync\nSetting the performance standard: make sure optimized configurations are tested, accessible, and adaptable to research tasks so that the whole team can work at maximum velocity\nEstablishing the Pareto frontier: communicate with researchers about throughput/accuracy tradeoffs (e.g. lower-precision compute, higher async staleness) to establish optimal points\nWhat You'll Need to Succeed\nBS/MS in Computer Science or a related quantitative field and 4+ years working as a software engineer or machine learning engineer, or PhD in a relevant research area and 1+ years work experience.\nExperience with LLM inference libraries such as vLLM and training libraries such as Megatron, including usage and throughput optimization.\nExperience running LLM inference and/or training at scale across multi-node GPU clusters.\nFamiliarity with core concepts in reinforcement learning.\nProven track record of software engineering excellence - clean APIs, well-tested code, thoughtful abstractions.\nHands-on experience supporting and maintaining production libraries used by other engineers and researchers.\nStrong CI/CD skills - experience designing integration test suites, build pipelines, and automated quality gates for multi-package ecosystems.\nA passion for agentic systems and awareness of cutting-edge approaches in LLM tool use and autonomy.\nBonus Points For\nExperience working with kernel DSLs and frameworks like CUDA, CuTe, CuTile, Triton, etc.\nExperience with GPU networking frameworks like NCCL and NIXL, as well as fabrics like NVLink, IB, and EFA.\nExperience with distributed systems or compute frameworks (Ray, Flyte, Airflow, or similar).\nFamiliarity with containerized compute backends (Docker, Modal) and sandbox orchestration.\nExperience with Ray actors, Ray Serve, or similar actor-model frameworks.\nCompensation\nWe offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.\nU.S. Benefits. Full-time U.S. employees receive a comprehensive benefits program including medical, dental, and vision coverage; employer-paid life and disability insurance; flexible time off with generous company wide holidays; paid parental leave; an educational assistance program; commuter benefits, including bike share memberships for office based employees; and a company subsidized lunch program.\nInternational Benefits. Full-time employees outside the U.S. receive a comprehensive benefits program tailored to their region. USD salary ranges apply only to U.S.-based positions; international salaries are set to local market.\nExpected Base Salary Range\n$148,000—$240,000 USD\nAbout LILA\nLila Sciences is building Scientific Superintelligence™ to solve humankind's greatest challenges. We believe science is the most inspiring frontier for AI. Rather than hard-coding expert knowledge into tools, LILA builds systems that can learn for themselves.\nLILA combines advanced AI models with proprietary AI Science Factory™ instruments into an operating system for science that executes the entire scientific method autonomously, accelerating discovery at unprecedented speed, scale, and impact across medicine, materials, and energy. Learn more at www.lila.ai.\nGuided by our core values of truth, trust, curiosity, grit, and velocity, we move with startup speed while tackling problems of historic importance. If this sounds like an environment you'd love to work in, even if you don't meet every qualification listed above, we encourage you to apply.\nWe’re All In\nLila Sciences is committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status.\nInformation you provide during your application process will be handled in accordance with our Candidate Privacy Policy.\nA Note to Agencies\nLila Sciences does not accept unsolicited resumes from any source other than candidates. The submission of unsolicited resumes by recruitment or staffing agencies to Lila Sciences or its employees is strictly prohibited unless contacted directly by Lila Science’s internal Talent Acquisition team. 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