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. Contribute to development of state-of-the-art models to solve challenging real-world scientific problems in healthcare.

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 clusters 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

• PhD or master's in computer science, engineering, applied mathematics, machine learning, or equivalent practical experience.
• 2+ years experiences in cell imaging (required for master's degree holders).
• 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.

Skills

• 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.
• Commit early and often mentality, metrics before models, and shipping high quality production code.

Education

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