AI/ML Engineer - Phenomics

Role Summary

AI/ML Engineer - Phenomics at GSK. Join the AI/ML Phenomics Team to apply cutting-edge machine learning and AI methodologies to generate insights from multi-modal high-content data modalities, including spatial transcriptomics, high-dimensional imaging, and multi-omics readouts in in vitro cellular systems. This role focuses on developing and validating state-of-the-art ML models to solve challenging real-world scientific problems and to impact target identification, hit identification, and safety testing.

Responsibilities

• Carry out product-driven research on novel machine learning methods to analyze terabytes of internal multi-modal high-content data.
• Design approaches to deconvolve real biological signals from confounding effects in high-throughput biological data.
• Leverage internal HPC and cloud compute to train and productionize models at scale.
• Work closely with domain experts on cross-disciplinary teams to generate actionable insights that impact target identification, hit identification, and safety testing.
• Contribute to the developing codebase with well-tested, production-ready code.

Qualifications

• Required: PhD or master's in computer science, engineering, applied mathematics, machine learning, or equivalent practical experience.
• Required: 2+ years of experience in machine learning and software engineering best practices.
• Required: 2+ years of experience with developing, implementing, and training deep learning models with PyTorch, TensorFlow, or other frameworks.
• Required: 2+ years of experience in a collaborative CI/CD software development environment, including use of git.
• Required: 2+ years of experience in cell imaging is required for master‚Äôs degree holders.

Skills

• Experience with high-content imaging and diverse multi-omics (preferred).
• Knowledge in disease biology, molecular biology, and biochemistry (preferred).
• Track record of writing software in a team in industrial environments or open-source projects (preferred).
• Track record of projects or peer-reviewed publications at the intersection of machine learning and life sciences (preferred).
• Ability to commit early and often, focus on metrics before models, and ship high-quality production code (preferred).

Education

• PhD or master's in computer science, engineering, applied mathematics, machine learning, or equivalent practical experience (required).