About the job
Your Impact at Lila
Join our dynamic team in the Physical Sciences division as a Senior Machine Learning Engineer. In this pivotal role, you will be instrumental in shaping the infrastructure that empowers Lila’s scientific superintelligence to autonomously design, execute, and interpret intricate physics simulations. Your expertise will be critical in developing core systems that enable AI agents to reason over and control computational experiments, ranging from electronic structure calculations to surrogate-driven atomistic modeling, and beyond. Your contributions will directly enhance the capability of autonomous computational processes to explore chemical and materials landscapes with unmatched autonomy and robustness.
What You Will Be Building
- Architect and implement agentic frameworks to support dynamic, multi-stage simulation workflows for scientific tasks.
- Develop pipelines that allow agents to autonomously plan, schedule, execute, and interpret computational tasks at scale.
- Create integration layers and APIs that connect machine learning models, large-scale simulation engines, databases, and heterogeneous compute platforms.
- Collaborate with AI researchers to productionize agent behaviors, including tool-use strategies, simulation-aware decision loops, and adaptive task planning.
- Enhance the robustness, modularity, performance, and reproducibility of agent-driven computational workflows; develop internal tooling for debugging, observability, and validation.
What You’ll Need to Succeed
- A Master's degree or PhD in Computer Science, Machine Learning/AI, Scientific Computing, or a related technical field, or equivalent experience.
- Proven experience in building machine learning-driven pipelines, workflow systems, or tool-use frameworks, particularly for complex or scientific applications.
- Proficiency in Python and familiarity with machine learning ecosystems; experience with compiled languages such as C++, Rust, or Julia is advantageous.
- Understanding of large-scale scientific or engineering software, including integrating external tools into automated computational workflows.
- Background in distributed systems, high-performance computing environments, cloud platforms, or accelerator-based computing.
- A solid grasp of modern machine learning architectures and their deployment in production systems (e.g., GNNs, transformers, diffusion models, multimodal or tool-using models).
- Strong engineering fundamentals: reproducibility, testing, modular design, CI/CD, and scalable machine learning operations.
Bonus Points For
- Experience developing or integrating agentic frameworks, autonomous machine learning pipelines, or multi-step tool-using agents, particularly for scientific applications.
