A natural match if you have experience optimizing large language models on NVIDIA hardware and working in collaborative AI research environments.
Derived from job-description analysis by Serendipath's career intelligence engine.
Original posting from Lila Sciences via Greenhouse
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.
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.
Expected Base Salary Range$148,000—$240,000 USDAbout LILA
Lila 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.
LILA 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.
Guided 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.
We’re All In
Lila 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.
Information you provide during your application process will be handled in accordance with our Candidate Privacy Policy.
A Note to Agencies
Lila 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. Any resume submitted by an agency in the absence of a signed agreement will automatically become the property of Lila Sciences, and Lila Sciences will not owe any referral or other fees with respect thereto.