Full-stack ML Engineer

Valar Labs is a well funded, fast-growing AI diagnostics startup backed by leading VCs revolutionizing oncology and urology through cutting-edge technology and innovation. We are dedicated to improving patient outcomes through the development and deployment of novel AI-driven diagnostics to reduce treatment uncertainty for oncologists and patients. Our team comprises accomplished researchers, and passionate individuals committed to shaping the future of healthcare.

Full-stack ML Engineers at Valar Labs work at the intersection of machine learning research and software engineering. They take a model from an experiment to a product that oncologists use every day, and they own the systems that keep it running.

Key Responsibilities:

• Run fine-tuning and post-training experiments on our pathology and clinical models, from hypothesis to result
• Build the data loops that turn clinical and lab data into curated training and evaluation sets
• Develop survival and outcome models alongside medical oncologists on clinically relevant questions
• Deploy models into production and own their serving, monitoring and evaluation in a HIPAA environment
• Ship full stack features across our clinician portal, revenue cycle product and lab operations tooling
• Build agentic AI workflows that automate clinical and operational work, with the evals that make them trustworthy

Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Machine Learning or a related field, or equivalent practical experience
• Hands-on experience training and evaluating deep learning models in PyTorch
• Strong software engineering fundamentals and production experience, ideally with TypeScript, React, Node, Docker and AWS
• Genuine interest in both halves of the role: research depth and shipping products to real users
• Strong interest in deploying AI in healthcare, and in seeing it change how patients are treated
• Daily fluency with agentic coding tools such as Claude Code and Codex
• 1-4 years of experience; exceptional new grads welcome
• (Optional) Publications in medical AI or machine learning venues
• (Optional) Experience with pathology, medical imaging or clinical data