Machine Learning Scientist - Computational Chemist - Molecular Simulation - ML Force Field Scie

An early-stage biotechnology company is looking for a Machine Learning Scientist to help develop advanced simulation models for complex molecular systems.

This is a highly technical, hands-on role at the intersection of:

• Machine learning
• Molecular simulation
• Computational chemistry

The work is applied and experimental-focused
, meaning your models will directly inform real-world lab decisions rather than purely theoretical outputs.

You’ll join a small, collaborative team combining computational and experimental expertise, with the opportunity to take real ownership over model development and direction.

• Developing and refining machine learning models for molecular systems
• Running and analysing molecular simulations (MD / QM / DFT)
• Building and optimising custom force fields / learned potentials
• Benchmarking and improving model accuracy
• Scaling workflows using HPC / GPU systems
• Working closely with experimental teams to guide testing and validation
• Contributing to new methods, datasets, and IP development

• Strong background in computational chemistry / molecular simulation
• Experience with:
• Quantum chemistry / DFT / ab initio methods
• Exposure to machine learning applied to scientific or physical systems
• Comfortable working in a research-driven, technical environment
• Experience with:
• Force fields / potential models
• Complex materials or coordination chemistry
• Experience using tools such as:
• Exposure to HPC / GPU environments
• Background in early-stage or research-led environments

• PhD in Computational Chemistry, Physics, Materials Science, or similar
• or
• MSc with strong relevant research or industry experience

• Work on a highly novel problem space combining AI + chemistry
• Direct impact on real-world experimental outcomes
• High level of ownership and technical freedom
• Early-stage environment with strong growth potential
• Reduced working hours (32-hour week) supporting work-life balance

This role is ideal for someone who:

• Enjoys deep technical problem-solving
• Wants to bridge research and real-world application
• Is motivated by ownership and impact rather than pure production work

Please note:

due to the highly technical nature of this role, candidates should have demonstrable experience in molecular simulation and machine learning within a scientific context.