Senior Computational Biologist | ML Engineer

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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 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).

• 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)

• Build predictive models for:
  • molecule–microbe interactions
  • molecule–host pathway effects
  • omics signature prediction
  • clinical response forecasting
  • safety and develop ability scoring
• Translate model outputs into interpretable mechanistic narratives for R&D teams and external partners.

• 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

• 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

• 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…