Machine Learning Engineer II / Senior Machine Learning Engineer I, Physical Sciences
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Lilasciences · Cambridge, UKPrepare for this interview
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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.
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