Machine Learning Scientist – Generative Molecular Design

What you'll do

• Develop and optimize generative models, and connect them to property predictors and simulation-based rewards.
• Identify, ingest, quality-control, and validate training and evaluation data; develop workflows to generate and evaluate hypotheses.
• Deploy production-ready discovery tools and collaborate with ML, research, engineering, product, and discovery teams.

What they ask for

• At least 4+ years delivering generative or applied ML solutions for material discovery in industry or an equivalent research setting.
• Experience building scalable ML using PyTorch, TensorFlow, or JAX, and writing robust production-grade code.
• Familiarity with cloud platforms for compute, data processing, and storage.
• PhD or MSc in Computational Chemistry, Physics, Computer Science, or equivalent; strong communication and collaborative software-engineering experience.

Nice to have

• Active learning and Bayesian optimization using simulation or experimental oracles.
• Guided generative modeling or agentic and LLM approaches to material discovery.

Pay

$134.4K – $252K (base salary)

Tools

PyTorch, TensorFlow, JAX, Google Cloud Platform, AWS, DFT, molecular dynamics, LoRA

AI focus

Generative AI for material discovery, including diffusion, flow-matching, GFlowNets, LLM- and agent-based methods, reward-guided generation, and active learning.