Machine Learning Research Engineer - Training

Machine Learning Research Engineer - Training

Our team is partnered with a frontier small molecule discovery team based in San Francisco and New York, led by world-class researchers and engineers at the forefront of physics-informed machine learning and computational chemistry. Be part of a team redefining how molecules and materials are discovered through AI. This group is building large-scale models that capture the fundamental rules of chemistry and physics, enabling predictive and generative design at atomic resolution.


You'll collaborate with leading experts across machine learning, physical sciences, and engineering to create systems that not only model reality but accelerate experimental validation and scale through synthetic data. This is an opportunity to take ownership from concept to deployment and push the boundaries where advanced AI meets the structure of matter.

The Role

You'll design and scale training pipelines to push beyond current architectures. Your work will span:

• Developing robust workflows for large-scale pretraining and fine-tuning of physics-informed models.
• Architecting strategies for representation learning, uncertainty estimation, and multi-task objectives.
• Building interpretability and diagnostic tools to guide model improvements and ensure reproducibility.
• Collaborating with researchers to debug complex training dynamics and optimize performance across distributed systems.

What You'll Bring

• Strong experience in machine learning research engineering, with a focus on training large models.
• Deep understanding of optimization, architecture tuning, and representation learning.
• Proficiency in Python and ML frameworks such as PyTorch, JAX, or similar.
• Familiarity with distributed training environments (multi-GPU, multi-node) and workflow automation.
• Ability to translate scientific goals into scalable engineering solutions.

Why This Matters

You'll help define the playbook for training models that simulate the physical world with unprecedented speed and accuracy to advance applications from drug discovery to materials design.