Computational Biologist

Company profile: Biotech firm. The company was founded by PhD experts in genetics, epigenetics, and microbiome research and specialises in microbiome and human biomarker testing to assess the real-world impact of topical products across skin, scalp, and oral applications. The firm already has a global presence, with laboratories in Cambridge, New York City, and Singapore. To date, the company has collected over 50,000 human skin microbiome samples alongside clinically tested formulations, using this extensive genomic dataset to identify optimal formulations that may help address conditions such as acne, atopic dermatitis, and rosacea. It has received support from national research and innovation bodies and raised $5MM USD to date from international venture investors.

This firm is building a next-generation AI-driven discovery platform to identify and design novel functional actives, including peptides and complex ingredient systems. The platform integrates large-scale biological datasets (>50,000 samples and measurements) spanning multi-omics data, microbiome sequencing, clinical and real-world outcomes. Their goal is to translate biological signals into actionable compound discovery and optimisation, powering a pipeline across: Discovery → Prediction → Design → Validation.

They are looking for a Senior Computational Biologist with ML Engineering background to help build, bridge, and functionalise the link between AI-powered biological discovery and real-world clinical outcomes. This role sits across biological discovery and scalable ML engineering. You will own key parts of the end-to-end architecture from data to model to evaluation to deployment, and work closely with ML engineers and software engineers to productise the platform into client-ready outputs. This is a senior role with significant autonomy and technical ownership.

Key responsibilities:

• Build the discovery engine (data → signal → candidate)
• Develop models that identify novel functional actives from multi-omic datasets.
• Detect patterns in biological signatures that correlate with clinical outcomes (e.g., inflammation reduction, microbiome restoration, barrier repair, malodour reduction).
• Create robust feature representations from microbiome sequencing (16S/ITS/shotgun), gene expression / transcriptomics, lipidomics / proteomics / metabolomics, clinical metadata and response data, SNP and risk features (where relevant).
• Predict mechanism + response
• Build predictive models for molecule-microbe interactions, molecule-host pathway effects, omics signature prediction, clinical response forecasting, safety and developability scoring.
• Translate model outputs into interpretable mechanistic narratives for R&D teams and external partners.
• Design and optimise functional complexes
• Implement multi-objective optimisation and scoring frameworks to balance efficacy / predicted response, safety and stability constraints, manufacturability and cost, regulatory feasibility.
• Support generation of intelligent ingredient complexes, repurposed peptides, newly discovered natural peptides.
• Productionise the AI product launch
• Build end-to-end ML pipeline covering ingestion, training, evaluation and deployment.
• Develop APIs/services to serve predictions and ranked candidates into internal tools and client outputs.
• Create evaluation harnesses to compare predicted vs. observed validation outcomes.
• Implement monitoring and governance: drift, data quality checks, model versioning, auditability.

Remote with min requirement of 1 day per month in the Cambridge office.

Job requirements:

• Strong academic background (masters or PhD level) in Biological and Life Sciences, Bioinformatics, Computer Science, or similar.
• Strong Python and experience building ML systems end-to-end with evidence (links to shipped projects, tools, or repos).
• Proven ability to work with large, messy real-world datasets and to define leakage-safe validation splits (e.g., sample, time, cohort).
• Practical knowledge of ML evaluation, validation strategy, and failure modes including error analysis and iteration planning.
• Experience with model development using PyTorch / TensorFlow / JAX.
• Comfort deploying models (batch + real-time inference) into production environments or owning the handoff to engineering with clear interfaces.
• Experience with biological or high-dimensional scientific datasets (omics, imaging, microbiome, clinical).
• Experience with multi-modal learning, embeddings, or representation learning.
• Interest in mechanism-driven modelling and scientific interpretability.
• Foundation model experience (LLMs and/or biological foundation models) with proof (fine-tuning/adapters or applied FM pipelines, plus how it was evaluated).
• A strong portfolio of tool or pipeline development (internal platforms, evaluation harnesses, reproducible workflows).

Benefits of the job:

• A great starting salary of £55,000 - £95,000 DOE.
• Lovely offices in Cambridge with very hybrid working (min requirement 1 day per month).
• Access to their Equity Incentive Plan, allowing you to grow alongside the firm.
• Hands-on experience working with industry experts in a rapidly expanding biotech sector.
• The opportunity to help shape the brand of a company advancing microbiome science and its real-world applications.