Machine Learning Engineer, Physical Sciences AI
$180k - $298k • Cambridge, MA USA; San Francisco, CA USA • Full-time
Posted 8mo ago
This job has been open for more than 60 days. It may no longer be actively hiring, so check with the company before putting a lot into an application.
Job Location
Cambridge, MA USA; San Francisco, CA USA
Tech Stack
Remote Work Policy
On-site
Employment Type
Full-time
Categories
Machine Learning Engineer
About the job
Lila's Physical Science AI team leverages AI to accelerate discovery in materials and chemistry, contributing to the company's mission of building Scientific Superintelligence. As a Machine Learning Engineer, you will act as a crucial link between research and production, transforming models and agentic workflows developed by scientists and researchers into reliable, scalable systems. The role emphasizes strong software engineering skills and practical experience in deploying ML systems to production, with a scientific background being a beneficial but not essential attribute. Curiosity and a passion for scientific discovery are key to thriving in this position.
Responsibilities
- Own models and agentic workflows from research code to deployed, monitored services.
- Build the integration layer for AI agents to reliably call models, tools, simulations, and data services in production.
- Ensure correct model training and efficient execution of models and agents, including profiling and managing resource usage.
- Design and maintain production AI/ML pipelines covering data ingestion, training, evaluation, deployment, and monitoring, incorporating testing, observability, CI/CD, and automated evaluations.
- Collaborate with domain scientists, ML researchers, and platform engineers to influence the AI/ML platform architecture, coding standards, and engineering best practices.
Requirements
- 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 like PyTorch or Hugging Face.
- Experience deploying AI/ML services in cloud-based infrastructure (e.g., FastAPI, containers, orchestration, databases).
- Hands-on experience bringing models to production (deep learning, LLMs, multimodal, or RAG) with strong debugging, profiling, and optimization skills.
- Clear communication and collaboration skills in cross-functional settings.
Benefits
- Competitive base compensation with bonus potential.
- Generous early-stage equity.
- Comprehensive medical, dental, and vision coverage.
- Employer-paid life and disability insurance.
- Flexible time off with generous company-wide holidays.
- Paid parental leave.
- Educational assistance program.
- Commuter benefits, including bike share memberships for office-based employees.
- Company-subsidized lunch program.
- International benefits tailored to region.