Your Impact at LILA

Lila's Physical Science AI team uses AI to accelerate discovery in materials and chemistry. It’s a core part of Lila's mission to build Scientific Superintelligence and solve some of humanity’s most important challenges. As a Machine Learning Engineer, you’ll be the bridge between research and production. You’ll take models and agentic workflows built by our scientists and ML researchers and turn them into reliable, scalable systems that run on our platform every day. What matters most is strong software engineering and real experience shipping ML systems to production. A scientific background is a plus, but curiosity and a passion for discovery are what will make you thrive here.

What You'll Be Building

• Own models and agentic workflows end to end, from research code to a deployed, monitored service.
• Build the integration layer that lets AI agents reliably call models, tools, simulations, and data services in production.
• Make sure models train correctly and that models and agents run efficiently, including profiling memory and compute, catching issues early, and keeping resource usage in check as workloads grow.
• Design and maintain production AI/ML pipelines (data ingestion, training, evaluation, deployment, monitoring), with testing, observability, CI/CD, and automated evaluations built in so releases are safe and results are trustworthy.
• Partner with domain scientists, ML researchers, and platform engineers, and help shape the architecture of our AI/ML platform, coding standards, and engineering 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) and experience with machine learning frameworks (e.g., PyTorch, Hugging Face).
• Experience deploying AI/ML services in cloud-based infrastructure (e.g., FastAPI, containers, orchestration, databases).
• Hands-on experience bringing models to production (e.g., deep learning models, LLMs, multimodal models, or RAG), with strong debugging, profiling, and optimization skills.
• Clear communication and collaboration in cross-functional settings.

Bonus Points For

• Contributions to open-source ML or scientific software.
• Experience or strong interest in building with AI agents (e.g., tool calling, multi-step workflows).
• Experience with workflow orchestration, data provenance, or large-scale compute environments.
• Exposure to scientific domains (materials, chemistry, or physics) and their data formats, simulations, or benchmarks.
• Performance and memory optimization for training or inference (e.g., mixed precision, distributed training, GPU profiling, efficient model serving).