Bio-ML Scientist – Metabolic Modelling

Ellison Institute of Technology Oxford

. Research, design, and build AI and machine learning systems addressing research challenges in synthetic biology, genome design, and molecular evolution .

Posted 10/9/2026full-timeOxford • United KingdomMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in AI and machine learning systems applied to synthetic biology and computational biology, with strong capabilities in building robust ML training pipelines and collaborating within multidisciplinary teams. Proficient in scientific programming languages and effective communication of complex concepts to diverse audiences.

Highest-signal resume keywords

PhD Degree in Relevant FieldMachine Learning Model DevelopmentScientific Programming (Python, R, Julia)Computational Biology ExpertiseTime Management Across Competing Tasks

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine LearningComputational BiologyData VisualizationData AnalysisML Training PipelinesVersioning and DocumentationGenome DesignMolecular EvolutionAutomated Model ConstructionConstraint-Based Modelling

Soft Skills

Excellent Communication SkillsTeam CollaborationRapid Context SwitchingProblem-SolvingOrganizational Skills

Tools & Technologies

Bioinformatics PlatformsScientific Compute PlatformsMLOps ToolsFlux Balance AnalysisSensitivity Analysis

Industry Keywords

Synthetic BiologyGenome-Scale Metabolic ModelsOmics DataBiochemistryMathematicsComputer ScienceEngineering

Tech Stack

Tools & technologies

FluxPythonSwitching

About the role

Key responsibilities & impact

Research, design, and build AI and machine learning systems addressing research challenges in synthetic biology, genome design, and molecular evolution
Lead and contribute to collaborative projects with ML Engineering team members, GBI researchers and staff, EIT colleagues, and external collaborators
Work closely with GBI scientists to co-create research projects and develop fit-for-purpose computational solutions
Provide expert computational biology knowledge to GBI researchers and scope novel research avenues
Interact with Bioinformatics and Scientific Compute platform teams to support GBI data flows and MLOps
Ensure robust and reproducible ML training pipelines, with versioning and documentation of data, models, and code
Keep abreast of progress in AI and biology and use strategic learning opportunities
Lead and contribute to research publications in prestigious venues
Organise and prioritise work across multiple competing deadlines

Requirements

What you’ll need

PhD degree in a suitable field including molecular biology, biochemistry, mathematics, computer science, computational biology, engineering, or related discipline
Experience in building machine learning models for biological design or discovery tasks, involving processing, visualizing, and analysing various data modalities
Ability to abstract high-level biological questions and translate them into actionable computational tasks, evidenced by previous achievements in a comparable industry role or a promising publication record in scientific journals and technical conferences
Ability to learn quickly and dive into a range of problem spaces and computational methods
Ability to work and communicate with and within diverse and multidisciplinary teams
Fluency in one or more scientific programming languages, such as Python, R, or Julia, with experience in best practices in machine learning, including documentation
Excellent written and oral communication skills for diverse audiences, including colleagues without a computational background
Excellent time management skills across competing tasks requiring rapid context switching
Right to work permanently in the UK; sponsorship may be considered case-by-case
Must live in or within easy commuting distance of Oxford, or be willing to relocate
Desirable: two years of industry or postdoctoral experience in similar roles
Desirable: experience integrating machine learning methods with genome-scale metabolic models
Desirable: experience with software pipelines for genome-scale metabolic modelling, including automated model construction from ‘omics data, flux balance analysis, flux sampling, sensitivity analysis, and related constraint-based modelling methods

Benefits

Comp & perks

Travel Allowance
Bonus
Pension - Employer contribution 7.5%, minimum employee contribution 5%
Life Assurance
Income Protection
Private Medical Insurance as standard for you, your partner and any dependents, including hospital Cash Plan
Employee discounts
Electric car scheme
Nursery Salary Sacrifice scheme
Cycle to Work Scheme
Family Planning
Neurodiversity support including advice and assessments
Coaching & Therapy services
Hybrid working model