Senior Machine Learning Engineer, Physical Sciences
Technologies
PyTorch · Huggingface · FastAPI · GRPC · containers · orchestration · cloud infra · LLMs · multimodal models · RAG
About Lila Sciences
Autonomous AI platform that executes the scientific method end-to-end—from hypothesis generation to experimental execution—for biotech, materials, and chemical R&D.
Series A
Job description
The full responsibilities and requirements are on the employer’s site.
Read the job description ↗Source and classification
Internal deployment & tooling · Evidence for this classification:
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
More from the job description
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 clou
Employer postings · Data from · Sources