Senior ML Research Engineer: Foundation Models

Genesis Therapeutics; San Mateo (CA); Sur place; USD 180 000 - 260 000; Plein temps

Résumé du poste

Genesis Molecular AI is seeking an ML Research Engineer to bridge fundamental ML research and production-grade systems, focusing on foundation models for molecular science. You will collaborate with ML researchers, computational chemists, and drug discovery scientists to translate cutting-edge ideas into scalable pipelines, pretraining, and deployment-ready models that power real discovery programs.

Positions are available at Senior, Staff, and Principal levels, with a strong emphasis on code

Qualifications

• 2+ years industry experience building complex ML systems.
• Deep expertise in scalable foundation models, pretraining/posttraining, and related systems.
• Strong coding skills across the ML stack: Python, PyTorch, distributed training.

Responsabilités

• Drive the R&D and scaling of foundation models.
• Implement, optimize, and build production-ready models from research prototypes.
• Optimize performance of large-scale ML systems, incl. distributed training and GPU optimizations.
• Bridge ML and computational chemistry workflows to deploy models into drug discovery pipelines.
• Own end-to-end experimental lifecycle with rigorous analysis to guide next steps.
• Collaborate with researchers and engineers to integrate models into the discovery platform.

Connaissances

Foundation models

Python

PyTorch

Distributed training

Code quality

Outils

Pearl

Git

CUDA

Description du poste

Genesis Molecular AI is seeking an ML Research Engineer to bridge fundamental ML research and production-grade systems, focusing on foundation models for molecular science. You will collaborate with ML researchers, computational chemists, and drug discovery scientists to translate cutting-edge ideas into scalable pipelines, pretraining, and deployment-ready models that power real discovery programs.

Positions are available at Senior, Staff, and Principal levels, with a strong emphasis on code