AI/ML Engineer - Phenomics

Role Summary

AI/ML Engineer - Phenomics at GSK will apply cutting-edge machine learning and AI methodologies to generate insights from multi-modal high-content data, including spatial transcriptomics, high-dimensional imaging, and multi-omics readouts from in vitro screens. You will work with domain experts to translate complex data into actionable biology insights that inform target identification, hit identification, and safety assessments. This role is part of the AI/ML Phenomics Team and involves collaborating across sites including San Francisco, Cambridge, London, Seattle, Stevenage, and Upper Providence.

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 that are inherent in high-throughput biological data.
• Leverage internal high performance computing cluster and cloud compute to train and productionize our 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 our 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 cell imaging (for master's degree holder).
• Required: 2+ years of experience in machine learning and software engineering best practices.
• Required: 2+ years of experience with working in a collaborative CI/CD software development environment, including use of Git.
• Required: 2+ years of experience with developing, implementing, and training deep learning models with PyTorch, TensorFlow, or other deep learning frameworks.
• Preferred: Experience working 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: Mentality of commit early and often, metrics before models, and shipping high quality production code.