Applied AI Engineer: Build Production ML for Life Sciences

GSK plc; South San Francisco (CA); On-site; USD 136,000 - 227,000; Full time

Benefits offered by this job

Health care

Retirement benefits

Paid holidays

Vacation

Paid caregiver/parental and medical le

Job summary

GSK plc is seeking an Applied AI Engineer to embed with cross‑functional teams and deliver practical AI/ML solutions aligned with R&D and business priorities. You will design, build, and deploy models and AI tools to accelerate discovery and decision‑making.

The role blends hands‑on engineering with advisory work, developing reusable patterns, pipelines, and knowledge transfer while ensuring privacy, ethics, and regulatory considerations.

Qualifications

• Bachelor’s degree in CS/ML/related field or equivalent professional experience.
• 2+ years of professional ML development and deployment experience.
• Proficiency with Python and ML libraries (PyTorch, TensorFlow, scikit-learn).
• Experience with cloud platforms (GCP, AWS, or Azure) and containerization (Docker, Kubernetes).
• Ability to communicate complex concepts to non-technical stakeholders.

Responsibilities

• Advise business units on AI/ML use cases, feasibility, and deployment options.
• Co-design PoCs with product and domain teams to validate ideas.
• Translate requirements into scoped technical solutions with measurable criteria.
• Build, train, evaluate, and iterate ML models for real-world tasks.
• Package models into production services (APIs, containers) on cloud.

Skills

Python

ML/DL

Cross-functional teamwork

Model deployment

NLP/LLM basics

GCP/AWS/Azure

Education

Bachelor’s degree in CS/ML/related

Tools

PyTorch

TensorFlow

Keras

Docker

Kubernetes

MLflow

Job description

GSK plc is seeking an Applied AI Engineer to embed with cross‑functional teams and deliver practical AI/ML solutions aligned with R&D and business priorities. You will design, build, and deploy models and AI tools to accelerate discovery and decision‑making.

The role blends hands‑on engineering with advisory work, developing reusable patterns, pipelines, and knowledge transfer while ensuring privacy, ethics, and regulatory considerations.