AI/ML Engineer - Phenomics

Role Summary

AI/ML Engineer – Phenomics. You will 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, ranging from spatial transcriptomics of proprietary patient cohorts to high-dimensional imaging and multi-omics readouts from high-throughput genetic and chemical perturbation screens in vitro cellular systems. Location: USA, UK, and Europe sites including San Francisco, Cambridge, Heidelberg, 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 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 holders).
• Required: 2+ years of experience with machine learning and software engineering best practices.
• Required: 2+ years of experience in a collaborative CI/CD software development environment, including use of Git.
• Required: 2+ years of experience 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.