ML Research Engineer, Performance Optimization

Title: ML Research Engineer, Performance Optimization Location: Cambridge, MA USA; San Francisco, CA USA

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.

What You'll Be Building

• GPU compute performance: profiling, kernel development, long-context handling, memory footprint optimization
• GPU cluster performance: collective/MoE framework optimization, comm/compute overlap, RDMA/IB/EFA usage, P/D disaggregation
• 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.