BS/MS in Computer Science or related field with 4+ years of experience, or PhD with 1+ year of experience.
Proficiency with LLM inference libraries like vLLM and training libraries such as Megatron.
Experience with multi-node GPU clusters for LLM inference and training.
Familiarity with reinforcement learning concepts.
Strong software engineering skills with clean APIs and well-tested code.
Experience maintaining production libraries for engineers and researchers.
Strong CI/CD skills for multi-package ecosystems.
Responsibilities
Maximize GPU compute performance through profiling and kernel development.
Optimize GPU cluster performance and manage collective/MoE frameworks.
Handle big data management during mid-training across various processes.
Optimize the async RL pipeline and manage environment interfacing.
Set performance standards for optimized configurations accessible to the research team.
Communicate throughput/accuracy tradeoffs to establish optimal performance points.
Benefits
Comprehensive medical, dental, and vision coverage.
Employer-paid life and disability insurance.
Flexible time off with generous company-wide holidays.
Paid parental leave and educational assistance program.
Commuter benefits, including bike share memberships.
Company-subsidized lunch program.
Full Job Description
Your Impact at LILA
Lila 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.
Big data handling: manage mid-training scale data across storage, streaming, tokenization, and processing
RL pipeline: optimize the async RL cycle, including environment/sandbox interfacing, long rollout handling, large token metadata (such as router replay), and weight sync
Setting 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
Establishing the Pareto frontier: communicate with researchers about throughput/accuracy tradeoffs (e.g. lower-precision compute, higher async staleness) to establish optimal points
What You'll Need to Succeed
BS/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.
Experience with LLM inference libraries such as vLLM and training libraries such as Megatron, including usage and throughput optimization.
Experience running LLM inference and/or training at scale across multi-node GPU clusters.
Familiarity with core concepts in reinforcement learning.
Proven track record of software engineering excellence - clean APIs, well-tested code, thoughtful abstractions.
Hands-on experience supporting and maintaining production libraries used by other engineers and researchers.
Strong CI/CD skills - experience designing integration test suites, build pipelines, and automated quality gates for multi-package ecosystems.
A passion for agentic systems and awareness of cutting-edge approaches in LLM tool use and autonomy.
Bonus Points For
Experience working with kernel DSLs and frameworks like CUDA, CuTe, CuTile, Triton, etc.
Experience with GPU networking frameworks like NCCL and NIXL, as well as fabrics like NVLink, IB, and EFA.
Experience with distributed systems or compute frameworks (Ray, Flyte, Airflow, or similar).
Familiarity with containerized compute backends (Docker, Modal) and sandbox orchestration.
Experience with Ray actors, Ray Serve, or similar actor-model frameworks.
Compensation
We offer competitive base compensation with bonus potential and generous early-stage equity. Your final offer will reflect your background, expertise, and expected impact.
U.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.
International 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.
