Summary: The Principal Machine Learning Engineer will lead the technical direction for ADMET modeling efforts within a drug discovery platform, focusing on machine learning applications in the life sciences. This role involves designing and implementing ML solutions, mentoring team members, and contributing to strategic decisions while collaborating cross-functionally. The position is fully remote and offers significant influence over technical direction without direct people management responsibilities. The contract length is between 6 to 12 months, with a competitive daily rate.

Key Responsibilities:

• Lead the design and implementation of ML solutions for ADMET using advanced techniques such as graph neural networks and transformers.
• Develop and extend models for specific applications, including data distillation, benchmarking, and evaluation.
• Define preprocessing and harmonization strategies for diverse assay datasets used in ADMET modeling.
• Build and maintain scalable, production-grade ML pipelines for training, inference, and deployment.
• Collaborate cross-functionally to ensure ML efforts align with real-world drug discovery needs.
• Mentor team members in designing and executing complex modeling projects in structural biology.
• Contribute to strategic decisions regarding ML infrastructure, model architecture, and deployment processes.
• Author or contribute to scientific publications or open-source software where appropriate.

Key Skills:

• PhD (or equivalent experience) in machine learning, computational biology, computational chemistry, or cheminformatics.
• Strong track record applying ML to real-world drug discovery or pharmaceutical R&D problems.
• Deep experience building and deploying ADMET-focused models using graph neural networks and transformer architectures.
• Proficiency with PyTorch or PyTorch Lightning and experience in designing large-scale model training workflows.
• Advanced understanding of assay protocols and methods for harmonizing heterogeneous ADMET datasets.
• Proven ability to deliver ML systems at scale, including distributed GPU training, CI/CD, model versioning, and deployment.
• Comfort working with modern MLOps tooling, including Docker, Kubernetes, cloud environments, and orchestration platforms.
• Strategic thinking with the ability to deconstruct complex modeling goals and drive them forward independently.
• Clear understanding of the role of ADMET models within drug discovery workflows and their impact on decision-making.

Salary (Rate): 1200

City: undetermined

Country: United Kingdom

Working Arrangements: remote

IR35 Status: outside IR35

Seniority Level: undetermined

Industry: IT