Machine Learning Scientist, Generative Molecular Design (Material Discovery)

What you'll do

• Develop and optimize generative models and the platform for navigating chemical space.
• Integrate property predictors and simulation-derived objectives into the generation reward interface.
• Identify, ingest, quality-control, and validate data; deploy workflows for discovery scientists.

What they ask for

• At least 4+ years delivering generative or applied ML solutions for material discovery in industry or an equivalent research setting.
• Ability to build scalable ML using PyTorch, TensorFlow, or JAX, and write robust production-grade code.
• Familiarity with cloud platforms such as Google Cloud Platform or AWS.
• PhD or MSc in Computational Chemistry, Physics, Computer Science, or equivalent.

Nice to have

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

Pay

$134.4K – $252K • Multiple Ranges (base salary; posting does not specify currency code)

Tools

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

AI focus

Develop generative AI and ML for material discovery, including diffusion, flow-matching, GFlowNets, LLM- and agent-based methods, reward modeling, and optional active-learning or Bayesian-optimization approaches.