AI/ML Engineer - Phenomics

Role Summary

AI/ML Engineer - Phenomics at GSK focusing on applying cutting-edge ML/AI to generate insights from multi-modal high-content data, with work spanning spatial transcriptomics, multi-omics readouts, and high-throughput screens in in vitro cellular systems.

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

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

Preferred Qualifications

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