Senior Computational Biologist | ML Engineer in Cambridge

Sequential 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. Our goal is to translate biological signals into actionable compound discovery and optimisation, powering a pipeline across: Discovery → Prediction → Design → Validation.

We 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.

The Data You Will Work With:

• The platform is built on a growing dataset of >50,000 biological samples and measurements, including paired pre- and post-treatment observations.
• The data includes multiple modalities such as microbiome sequencing (16S rRNA sequencing, ITS sequencing, shotgun metagenomics), Multi-omics (proteomics, lipidomics, metabolomics), Clinical and observational data (treatment exposure, formulation and ingredient combinations, clinical outcomes, patient metadata).
• Datasets include longitudinal measurements, enabling analysis of biological response to interventions (e.g., ingredient exposure, treatment, formulation).

1) 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)

2) Predict mechanism + response

• Build predictive models for:

• molecule–microbe interactions
• molecule–host pathway effects
• omics signature prediction
• clinical response forecasting
• safety and developability scoring

3) 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

• intelligent ingredient complexes
• repurposed peptides
• newly discovered natural peptides

4) 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.

5) Collaborate cross-functionally

• Work closely with biology, formulation, and clinical teams to design experiments and validation loops.
• Partner with product and commercial teams to shape “client-ready” deliverables (e.g., ranked actives, evidence packs, scientific dossiers).
• Lead and/or partner with ML and software teams to define ownership boundaries, code reviews norms, and the path from prototype to a maintained service.

12-Month Mission:

• Month 0–3: Foundation & Proof of Concept

• Establish harmonised datasets and core data pipelines with dataset versioning, documented schemas, and baseline QC checks.
• Deliver feasibility screening models for active discovery with an agreed split strategy (sample, cohort, and time splits) and reporting baseline ranking metrics (e.g., hit-rate@K, NDCG@K).
• Build initial predictive baselines with clear metrics.

• Implement multi-criteria scoring + optimisation with a defined objective, weighting strategy, and ablation plan.
• Extend predictive models and improve candidate ranking performance.
• Develop reproducible experiment tracking and evaluation workflows.

• Compare predictions against real validation outputs and refine models.
• Improve robustness, interpretability, and governance.
• Deliver a performance report suitable for internal and external stakeholders.

• Finalise v1.0 AI product outputs (ranked candidates + evidence summaries).
• Support pilot client projects and accelerate validation turnaround time.
• Contribute to the first commercial-ready “hero” functional complex pipeline.

What we’re looking for:

Essential:

• 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.
• Ability to communicate clearly across technical + scientific stakeholders.
• Strong experience leading or coordinating cross-functional teams, and thinking strategically across product and science (prioritisation, tradeoffs, and shipping).
• Comfort deploying models (batch + real-time inference) into production environments or owning the handoff to engineering with clear interfaces.

Strongly preferred:

• Experience with biological or high-dimensional scientific datasets (omics, imaging, microbiome, clinical).
• Experience with multi-modal learning, embeddings, or representation learning.
• Familiarity with optimisation / ranking / multi-objective scoring systems.
• 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).

Nice to have:

• Experience building developer-friendly tooling: CLIs, dashboards, APIs, or reusable libraries used by other scientists/engineers.
• Some front-end/product sense (you care about shipping usable tools, not just notebooks).
• Experience with causal inference, Bayesian methods, or mechanistic simulation.