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

ML Research Engineer, Performance Optimization

Responsibilities

Qualification

Required

Preferred

Benefits

Company

Lila Sciences

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