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As an ML Research Engineer, you'll design experiments, implement model architectures, and engineer scalable libraries to advance molecular simulations to support drug discovery.

The ML Research Engineer will develop training pipelines for atomistic simulation models and collaborate with researchers to optimize training workflows.

You will design and optimize distributed compute infrastructure for ML applications, improve resource utilization, and collaborate with ML engineers on large-scale workloads.

This role involves shaping training data strategies for computational drug discovery, curating protein-ligand systems, and collaborating with model teams to enhance drug discovery tools.

The role involves architecting molecular machine learning systems, building workflows for data generation and training, and collaborating between research scientists and infrastructure teams.

Conduct research to develop generative and sampling models for molecular systems, integrating probabilistic inference, deep learning, and reinforcement learning. Design, train, prototype, and benchmark diffusion, autoregressive, flow-based, and latent-variable architectures. Map between simulation and reality, collaborate with chemists and physicists, and work with engineers to deploy scalable research on distributed compute. Contribute end-to-end from ideation to production in a hybrid San Francisco/New York environment.