Bioinformatics Lead (Biotech)

TL;DR

Bioinformatics Lead (Biotech): Will own and evolve the company’s end-to-end computational pipeline, from somatic variant calling through to neoantigen prediction with an accent on engineering discipline, reproducibility and infrastructure design. Focus on building a computational backbone that supports clinical decision-making, regulatory scrutiny and future product development.

Location: London, United Kingdom

Company

A well-funded biotechnology company developing personalised cancer vaccines designed around the unique mutational landscape of each patient’s tumour.

What you will do

• Design, implement and maintain production-grade pipelines spanning variant calling, annotation, filtering and prioritisation for immunogenicity assessment.
• Convert exploratory code into modular, version-controlled workflows with comprehensive logging, audit trails and reproducible environments.
• Evaluate and integrate tools for HLA typing and peptide–MHC binding prediction, applying a critical understanding of their assumptions and limitations.
• Define data models and interfaces that enable traceability from raw sequencing data through to vaccine candidate selection.
• Contribute to infrastructure decisions, from cloud architecture and compute optimisation to internal tooling that enables scientists to interrogate results effectively.

Requirements

• Deep experience in somatic variant calling, particularly in ctDNA, liquid biopsy or clinical sequencing contexts.
• Strong expertise in workflow orchestration and reproducible pipeline development using tools such as Nextflow or Snakemake.
• Fluency in immunogenomics workflows, including HLA typing and neoantigen prediction pipelines.
• Excellent software engineering practice in a scientific context: clean, maintainable code; structured data models; version control; and rigorous documentation.

Nice to have

• Experience operating within regulated or clinically oriented environments where auditability and traceability are essential.
• Familiarity with machine learning methods relevant to immunogenicity prediction and structured biological data.
• Experience designing cloud-native infrastructure and managing scalable compute for genomics workloads.

Culture & Benefits

• Opportunity to define the computational architecture underpinning a new class of personalised cancer therapies.
• Direct influence over how patient data is transformed into therapeutic candidates.
• Work alongside founders who combine deep biological insight with clinical ambition.
• Rare combination of intellectual depth and translational urgency.