Job Opportunities API

Your Impact at LILA The AI Research team is tackling one of the most exciting, open problems in AI: training LLMs to run long-horizon scientific discovery tasks. Our approach spans the full post-training stack - from SFT to asynchronous RL on agentic harnesses - teaching models to plan, use tools, and learn from experience in domains where the ground truth isn't a preference label, but a scientific result. We're rapidly growing our Research Engineering org and seeking talented engineers and ML practitioners across levels to design, build, and optimize systems to push this frontier: scaling post-training, sharpening reasoning, and unlocking compute-intensive agentic-harness training. This is a rare chance to join an early team with the autonomy, flexibility, and compute to tackle frontier science problems. We operate with high agency, and a bias toward execution. Below are several focus areas within the team. We ask that candidates select the stream that best matches their experience and excitement. Work Streams Stream A: GPU Optimization & Training Performance Maximize hardware utilization across 100B+ parameter asynchronous RL training runs. Responsibilities include profiling, performance optimization, custom kernel development, communication-computation overlap, and long-context throughput improvements. You set and maintain the performance baseline. Stream B: Stack & Infrastructure Own the post-training infrastructure end-to-end — supervised fine-tuning, asynchronous RL with tool integration, and data pipelines. Build modular, reproducible workflows with single-command execution. Manage upstream framework upgrades and deliver composable pipelines spanning Data, SFT, and RL stages. You work tightly with Research Scientists to develop and productionize novel algorithms to run at scale. Stream C: Model Experimentation Bring deep, hands-on experience training large language models. Lead experimentation on reasoning model development, including mixture-of-experts stabilization, curriculum design, and synthetic reasoning trace generation. You have a bias toward experimental design and tracking, and know how to prioritize runs that yield promising outcomes. Stream D: Evaluations & Benchmarks Design and build best-in-class scientific agentic benchmarks and harnesses, along with the dashboards and leaderboards that inform every training decision. You have experience working with well known public benchmarks and have spent time building bespoke agentic benchmarks and harnesses. Stream E: Agentic Capabilities & Frontier Research Train models capable of planning, exploration, and tool use over extended horizons. Advance the state of the art in RL at scale with tool-calling, subgoal decomposition, and shared memory/skills across trials to expand the frontier of scientific agent capabilities. What You'll Need to Succeed Strong software engineering skills in Python; C++/CUDA a plus Experience with distributed ML training frameworks (Megatron-LM, TorchTitan, DeepSpeed, Ray) Understanding of large-scale model training techniques for 100B+ models Experience with cloud or HPC environment Ability to communicate technical results to internal and external stakeholders Bonus Points For Prior work with large scale scientific datasets or domain-specific modeling Contributions to open-source ML frameworks Experience with RL post-training (RLHF, GRPO, tool-augmented RL) Experience training MoE architectures Location San Francisco, CA or Cambridge, MA (Remote, Hybrid, and On-Site available depending on team needs). 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 disab