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Job Description
About the Role
Sequential is building a next‑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
- Translate model outputs into interpretable mechanistic narratives for R&D teams and external partners.
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
- Support of:
- 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
Month 3–6: Modelling & Optimisation
- 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
Month 6–9: Advanced Validation
- Compare predictions against real validation outputs and refine models
- Improve robustness, interpretability, and governance
- Deliver a performance report suitable for internal and external stakeholders
Month 9–12: Client Readiness & Pilot Launch
- 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, trade‑offs, and shipping)
- Comfort deploying models (batch + real‑time inference) into production environments or owning the handoff to engineering with clear interfaces
Strongly
- 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
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