AI/ML Engineer for Drug Discovery & Genomics

GlaxoSmithKline; San Francisco (CA); Sur place; USD 136 000 - 227 000; Plein temps

Avantages offerts par ce poste

Annual bonus

Health benefits

Retirement benefits

Paid holidays

Résumé du poste

GlaxoSmithKline is seeking a seasoned Machine Learning Engineer in San Francisco to design agentic AI systems and ML models that illuminate molecular disease mechanisms and accelerate therapy development. You will work with frontier and foundational genomics models on multiomics data from patients and experiments to inform disease understanding, drug design, and response.

Strong candidates have shipped high-performing AI/ML products; a passion for solving hard AI/ML problems and continuous

Qualifications

• Degree in a quantitative or engineering discipline or equivalent work experience.
• Experience with LLM agents, evals, and agentic workflow design.
• Experience in at least one major deep learning framework (PyTorch, TensorFlow).
• Programming in Python; experience with AI/ML stack tooling.

Responsabilités

• Imagine and engineer agentic systems and ML models for disease mechanisms and therapy development.
• Work with frontier models and multiomics datasets to model biology and medicinal chemistry.
• Ship high-performing AI/ML products and contribute to production-ready code.

Connaissances

Artificial Intelligence

AI Ethics

Classification models

Deep learning

Intelligent automation

Machine learning

Model evaluation

Model validation

Predictive modeling

Probabilistic modeling

Python

Git

SQL

Shell scripting

Pytorch

TensorFlow

Formation

Degree in a quantitative or engineering discipline or equivalent work experience

Outils

PyTorch

TensorFlow

Git

SQL

Shell scripting

Description du poste

GlaxoSmithKline is seeking a seasoned Machine Learning Engineer in San Francisco to design agentic AI systems and ML models that illuminate molecular disease mechanisms and accelerate therapy development. You will work with frontier and foundational genomics models on multiomics data from patients and experiments to inform disease understanding, drug design, and response.

Strong candidates have shipped high-performing AI/ML products; a passion for solving hard AI/ML problems and continuous