Senior ML Research Engineer — Frontier Drug Discovery

Calico Life Sciences; South San Francisco (CA); On-site; USD 220,000 - 290,000; Full time

Job summary

Calico Life Sciences in South San Francisco is seeking Machine Learning Research Engineers to join our rapidly growing ML team. You will help establish the engineering culture for frontier ML research in drug discovery and translate research prototypes into scalable, production‑ready systems.

This high‑agency role requires strong Python and JAX or PyTorch expertise, hands‑on experience with transformers, diffusion networks or GNNs, and a willingness to work onsite at least four days per week.

Qualifications

• BS/MS with 7+ years or PhD with 4+ years of ML software engineering experience
• Expertise in Python and JAX or PyTorch
• Hands-on experience building/training/optimizing advanced ML architectures (transformers, diffusion networks, GNNs)
• Experience driving complex ML engineering projects from concept to production
• Must be willing to work onsite at least four days a week

Responsibilities

• Research Engineering: identify engineering gaps for expanding research capabilities, architect solutions for complex systems, build foundational libraries
• Model Implementation and Optimization: translate prototypes into optimized code on accelerators, explore model parallelism
• Force Multiplier: design evaluation frameworks, benchmarks, and scientific workflow tools to accelerate research lifecycle

Skills

Python

JAX

PyTorch

ML engineering

Model training

Education

BS/MS in ML/CS

PhD in related field

Tools

GCP

Docker

Kubernetes

Job description

Calico Life Sciences in South San Francisco is seeking Machine Learning Research Engineers to join our rapidly growing ML team. You will help establish the engineering culture for frontier ML research in drug discovery and translate research prototypes into scalable, production‑ready systems.

This high‑agency role requires strong Python and JAX or PyTorch expertise, hands‑on experience with transformers, diffusion networks or GNNs, and a willingness to work onsite at least four days per week.