Applied Machine Learning/ Scientist

S3 Science Recruitment; Greater London; Hybrid; GBP 60,000 - 100,000; Full time

Benefits offered by this job

Ownership over technical work

Competitive compensation package

Collaboration with multidisciplinary teams

Job summary

A technology-focused company in the UK seeks a professional to apply machine-learning methods to molecular datasets. The role requires experience in building robust solutions and collaboration with interdisciplinary teams. Responsibilities include designing modelling experiments, assessing model performance, and contributing to maintainable ML codebases. This position offers ownership over technical work and competitive compensation in a hybrid or remote working context.

Qualifications

• Hands-on experience applying machine-learning methods to real-world problems.
• Strong proficiency in Python and common ML frameworks.
• Experience writing production-quality or research-grade ML code.
• Experience writing and maintaining production-quality ML code.
• Experience working in interdisciplinary teams.
• Exposure to collaborative projects with external partners.
• Background in applied or translational ML research.

Responsibilities

• Apply machine-learning techniques to problems in molecular modelling.
• Design and execute modelling experiments including dataset preparation.
• Work closely with scientists and engineers to support research objectives.
• Contribute to the development of maintainable, reproducible ML codebases.
• Work closely with scientists, engineers, and other stakeholders to support research objectives.
• Assess model performance and identify areas for improvement in applied settings.

Skills

Experience applying machine-learning methods

Proficiency in Python

Experience with ML frameworks (e.g. PyTorch)

Background in applied or translational ML research

Production ML code

Interdisciplinary teamwork

External collaborations

Job description

Location: London / Flexible (Hybrid or Remote)

Contract: Full-time

About the organisation

We are a technology-focused company applying modern machine-learning techniques to problems in molecular science and early-stage drug discovery. Our goal is to translate recent advances in ML into practical tools that support scientific research and decision-making in real-world discovery programmes.

The team brings together experience in machine learning, software engineering, and the life sciences, and collaborates closely with internal scientific teams and external research partners.

The role

This role focuses on applying machine-learning methods to scientific and molecular datasets, with an emphasis on building robust, usable solutions rather than purely exploratory research. You will work across the full lifecycle of applied modelling projects, from understanding scientific objectives through to model development, evaluation, and integration into downstream workflows.

You will collaborate with researchers and engineers across multiple disciplines, contributing both technical expertise and practical insight into how ML systems perform in applied scientific settings.

Key responsibilities

• Apply machine-learning techniques to problems in molecular modelling and computational life sciences
• Design and execute modelling experiments, including dataset preparation, model training, fine-tuning, and evaluation
• Translate scientific questions into well-defined modelling approaches
• Contribute to the development of maintainable, reproducible ML codebases
• Work closely with scientists, engineers, and other stakeholders to support research objectives
• Assess model performance and identify areas for improvement in applied settings

Requirements

• Hands-on experience applying machine-learning methods to real-world problems
• Experience working with biological, chemical, or scientific data
• Strong proficiency in Python and common ML frameworks (e.g. PyTorch)
• Experience writing and maintaining production-quality or research-grade ML code
• Experience working in interdisciplinary teams
• Exposure to collaborative projects with external partners
• Background in applied or translational ML research

What’s on offer

• Opportunity to work on applied ML problems in a scientific discovery context
• High degree of ownership over technical work
• Collaboration with a multidisciplinary technical and scientific team
• Competitive compensation package, including equity participation