ML Research Engineer, Foundation Models (Senior / Staff / Principal)

What you'll do

• Develop and scale foundation models, including pretraining, post-training, experimentation, and performance optimization.
• Productionize and integrate models such as Pearl into drug discovery workflows.
• Collaborate with research and chemistry teams, mentor colleagues, and contribute to publications and conferences.

What they ask for

• 2+ years of industry experience building complex ML systems.
• Expertise in scalable foundation models, pretraining and post-training methods, and distributed training.
• Strong Python and PyTorch skills, with hands-on experience in PyTorch Lightning and Ray Distributed Training.
• Hands-on experience writing CUDA kernels or optimizing GPU workloads beyond standard frameworks.

Nice to have

• Research experience in LLMs, diffusion, reinforcement learning, or other generative or predictive ML methods.
• Experience with computational chemistry, drug discovery, molecular modeling, or related systems.

Tools

Python, PyTorch, PyTorch Lightning, Ray Distributed Training, PyTorch Geometric, CUDA, Triton, TensorRT

AI focus

Research, train, optimize, and productionize foundation models for molecular science and drug discovery, including generative and predictive models, reinforcement learning or post-training systems, and structure prediction.