Senior Machine Learning Computational Chemist - £60k - Manchester

Senior Machine Learning Computational Chemist

Salary: £60,000 per annum

Contract: Initial 12-month fixed-term contract

Location: Hybrid/remote, with occasional attendance at a Manchester office

Hours: Full time, minimum 32 hours per week across five days

The opportunity

An early-stage biotechnology company is looking for a Senior Machine Learning Computational Chemist to lead the development and application of machine-learning and computational modelling approaches for complex molecular systems.

The company is developing innovative biotechnologies for the selective binding and recovery of metals from waste streams, including electronic waste. This is a hands-on senior position offering ownership of technical strategy, scientific quality and delivery.

You will work closely with computational scientists, wet-lab researchers, founders and external stakeholders to guide experimental priorities, solve complex modelling challenges and identify opportunities for intellectual property generation.

Key responsibilities

• Own and develop the machine-learning and molecular-modelling roadmap, including priorities, milestones, validation standards and computing requirements.
• Develop, adapt and validate machine-learning models and force fields for molecular applications.
• Lead the generation and quality assurance of computational datasets.
• Run and interpret quantum chemistry calculations and molecular dynamics simulations.
• Model metal-containing, biomolecular and coordination-chemistry-rich systems.
• Benchmark computational models against appropriate theoretical and experimental references.
• Diagnose and resolve issues involving electronic structure calculations, SCF convergence, molecular dynamics, model training, data quality and HPC environments.
• Build and maintain reliable, scalable HPC and GPU workflows across cloud and research computing infrastructure.
• Automate scientific workflows, training pipelines, experiment tracking and performance monitoring.
• Translate modelling results, uncertainty and limitations into clear recommendations for experimental colleagues.
• Contribute to technical strategy, programme planning and potential intellectual property generation.
• Provide technical leadership through method selection, code review, mentoring and scientific problem-solving.

Essential experience

• Proven hands-on experience developing or adapting machine-learning models and force fields for molecular systems.
• Strong computational chemistry and molecular simulation experience, including quantum chemistry and molecular dynamics.
• Experience modelling metal-containing and biomolecular systems.
• Extensive, practical experience using HPC and GPU computing environments.
• Strong Python programming skills and experience automating scientific workflows.
• Experience using HPC schedulers such as Slurm or an equivalent platform.
• Experience building reproducible training or simulation pipelines.
• Strong understanding of metal coordination chemistry, including coordination motifs, oxidation states and binding geometries.
• Ability to troubleshoot complex problems independently across simulations, model training, datasets and computing infrastructure.
• Strong communication skills, particularly when explaining computational evidence, limitations and recommendations to experimental scientists and other stakeholders.
• The independence and adaptability needed to succeed in a fast-moving, early-stage environment.
• Early-stage biotechnology or startup experience.

Relevant technologies

Experience with some of the following would be particularly useful:

• CP2K
• ORCA
• VASP
• Quantum ESPRESSO
• NWChem
• Slurm or equivalent workload schedulers
• Docker
• Singularity or Apptainer
• Weights & Biases
• MLflow
• Scientific Python and molecular simulation software

Qualifications and experience

You should have one of the following:

• A relevant PhD plus at least three years of relevant industry or professional research experience; or
• A relevant MSc plus at least five years of relevant professional experience.

Desirable experience

• Protein or peptide design.
• Metal-protein or metalloprotein systems.
• Thermodynamic calculations and quantitative assessment of molecular interactions.
• Model uncertainty estimation and data-quality assessment.
• Experiment tracking and performance optimisation.
• Mentoring or technical leadership.
• Collaboration with wet-lab scientists.
• Intellectual property generation or research commercialisation.

What success will look like

During the first three to six months, the successful candidate will be expected to deliver:

• Validated molecular machine-learning models benchmarked against appropriate computational and experimental references.
• Reliable predictions that directly inform experimental decisions.
• Clearly documented model accuracy, uncertainty, assumptions and limitations.
• Reproducible computational workflows with appropriate quality controls.
• A clear technical roadmap covering milestones, risks and computing requirements.
• Effective knowledge transfer between computational and experimental teams.