Senior Machine Learning Engineer, Physical Sciences

Your Impact at LILA

This Machine Learning Engineer for the Physical Sciences team focuses on building and operating end-to-end, scalable machine learning workflows that solve a diversity scientific use cases in materials, chemistry and physical sciences. Your work will advance research efforts on state-of-the-art algorithms to build towards scientific superintelligence across today’s greatest challenges in physical sciences.

What You'll Be Building

• Design, implement, and maintain end‑to‑end ML pipelines (data ingestion, feature engineering, training, evaluation, deployment, monitoring).
• Productionize models and services with robust testing, observability, and documentation in collaboration with cross-functional software teams and build CI/CD workflows and automated evaluations to ensure safe, frequent releases.
• Collaborate with domain scientists and platform engineers to translate research insights into performant, scalable systems.
• Contribute to technical design reviews, coding standards, and mentoring of best practices.

What You’ll Need to Succeed

• BS/MS/PhD in Computer Science, Engineering, or a related quantitative field, or equivalent industry experience.
• Strong Python software engineering fundamentals (testing, packaging, typing); experience with machine learning frameworks (e.g., PyTorch, Huggingface, etc.).
• Experience deploying ML services to production in cloud-based infrastructure (FastAPI/GRPC, containers, orchestration, cloud infra).
• Hands‑on experience with model deployment in production systems (LLMs, multimodal models, databases, RAG) with strong debugging and profiling skills.
• Clear communication and collaboration in cross‑functional settings.

Bonus Points For

• Exposure to scientific or engineering domains (materials, chemistry, physics) and related data formats/benchmarks.
• GPU optimization experience (CUDA, Triton, compilation, distributed training).
• Prior contributions to open‑source ML or scientific software.
• Experience with workflow orchestration, data provenance, or large‑scale compute environments.