Your Impact at LILA

As a member of our team in the Physical Sciences division, you will shape the ML- and agent-driven infrastructure that enables Lila’s scientific superintelligence to autonomously construct, execute, and interpret complex physics simulations. Your work will focus on building the core systems that allow AI agents to reason over and control computational experiments, spanning from electronic structure calculations to surrogate-driven atomistic modeling, and beyond. Your work will directly influence how autonomous computational processes will explore chemical and materials landscapes with unprecedented autonomy and robustness.

What You'll Be Building

• Architect and implement agentic frameworks that support dynamic, multi-stage simulation workflows for scientific tasks
• Develop pipelines enabling agents to autonomously plan, schedule, execute, and interpret computational tasks at scale
• Build integration layers and APIs that connect ML models, large-scale simulation engines, databases, and heterogeneous compute platforms.
• Work with AI researchers to productionize agent behaviors, including tool-use strategies, simulation-aware decision loops, and adaptive task planning.
• Improve the robustness, modularity, performance, and reproducibility of agent-driven computational workflows; build internal tooling for debugging, observability, and validation.

What You’ll Need to Succeed

• MS/PhD or equivalent experience in Computer Science, ML/AI, Scientific Computing, or a related technical field.
• Strong experience building ML-driven pipelines, workflow systems, or tool-use frameworks, ideally for complex or scientific applications.
• Proficiency in Python and ML ecosystems; experience with one or more compiled languages (e.g., C++, Rust, Julia) is beneficial.
• Familiarity with large-scale scientific or engineering software, including integrating external tools into automated computational workflows.
• Experience with distributed systems, HPC environments, cloud platforms, or accelerator-based computing.
• Deep understanding of modern ML 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 ML operations.

Bonus Points For

• Experience developing or integrating agentic frameworks, autonomous ML pipelines, or multi-step tool-using agents, particularly for scientific applications
• Background in large-scale simulation frameworks, scientific workflow orchestration, or automated computational experiment platforms.
• Contributions to open-source ML infrastructure, workflow engines, agent frameworks, or scientific software ecosystems.
• Familiarity with data-centric engineering practices: streaming systems, provenance tracking, distributed metadata services, or large-scale data orchestration.
• Experience with workflow orchestration systems (e.g., Flyte, Prefect, Airflow) and container orchestration (Kubernetes).
• Familiarity with materials science simulation codes (VASP, LAMMPS) or workflow frameworks (atomate, aiida) is a plus but not required.
• Expertise in advanced ML techniques relevant to agentic reasoning (planning models, self-improving systems, multi-tool agents, RLHF/RLAIF workflows).