Senior ML Research Engineer — Atomistic Simulations

Achira; San Francisco (CA); On-site; USD 120,000 - 160,000; Full time

Job summary

A leading biotech firm in San Francisco is seeking an experienced ML Research Engineer to develop innovative training systems for atomistic simulation models. The ideal candidate knows the intricacies of machine learning, has strong PyTorch skills, and is comfortable with distributed cloud setups. Responsibilities include scaling training pipelines, defining strategies for deep models, and partnering with researchers to tackle complex problems in drug discovery. This role offers a unique chance to work on groundbreaking technologies, shaping the future of molecular modeling.

Qualifications

• Experience working closely with ML researchers.
• Ability to scale training workflows effectively.
• Understanding of training parameters and their effects.
• Fluent in distributed programming using PyTorch.

Responsibilities

• Develop next-generation training pipelines for deep simulation models.
• Define training strategies aligned with research goals.
• Build diagnostics tools for model performance.
• Partner with researchers to improve training workflows.
• Tune model architectures for optimal performance.
• Investigate unique representation learning for molecular data.

Skills

ML research understanding

Familiarity with training workflows

Expertise in PyTorch

DevOps knowledge

Problem-solving

Tools

Multi-node setups

Kubernetes

SLURM

Job description

A leading biotech firm in San Francisco is seeking an experienced ML Research Engineer to develop innovative training systems for atomistic simulation models. The ideal candidate knows the intricacies of machine learning, has strong PyTorch skills, and is comfortable with distributed cloud setups. Responsibilities include scaling training pipelines, defining strategies for deep models, and partnering with researchers to tackle complex problems in drug discovery. This role offers a unique chance to work on groundbreaking technologies, shaping the future of molecular modeling.