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IMU Biosciences
IMU Biosciences
11 – 50 Employees
B2BBiotechnologyHealthcare
IMU Biosciences is a London-based biotechnology company building high-dimensional immunology technologies for clinical research, diagnostics, and drug development. Its multi-omic, high-throughput platform analyzes more than 100 million immune data points from a 2 mL blood sample and distinguishes approximately 2,500 immune cell subsets in real time. The resulting population-scale immune dataset supports applications such as predictive screening for hematopoietic stem cell transplantation, graft rejection assessment in solid organ transplantation, and immune signatures linked to treatment response and adverse events in immuno-oncology. IMU Biosciences works with clinical research groups, pharmaceutical and biotech companies, and diagnostics partners to translate immune phenotyping into practical clinical and commercial tools.
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Bioinformatics Software Engineer – Flow Cytometry

Build and maintain regulated machine learning pipelines for flow cytometry at IMU Biosciences. The role covers annotation, model tracking, reproducibility, and compliance for a precision medicine platform.

GBUnited KingdomHybridFull-timeMiddleSeniorEnglish RequiredFull-stack EngineerSoftware Engineer
DockerNumpyPandasPythonPyTorchScikit-Learn

Description

  • Run automated cell annotation workflows on large cytometry datasets.
  • Produce cell membership files, cell subset ratio tables, and automated reports that flag outliers for quality control.
  • Apply locked automated gating ensembles to cross-validation and evaluation data partitions.
  • Track and document gating exposure metrics for each patient.
  • Set and lock baseline models and numerical stability criteria for flow cytometry feature extraction pipelines.
  • Maintain the formal model lifecycle registry, including versioning, freezing, and locking gating ensembles and candidate predictive models.
  • Refit shortlisted machine learning models on larger cohorts and freeze final models before blinded clinical evaluations.
  • Commit versioned code, pin Conda and Docker environment specifications, and log random seeds and model hyperparameters.
  • Act as the primary code reviewer across the computational pipeline.
  • Apply controls against data leakage, verify data integrity, and uphold modular coding standards.
  • Take part in Independent Method Reviews, traceability audits, and regulatory compliance checks.
  • Work with Machine Learning Engineers, Biostatisticians, and Clinical Scientists.
  • Report to the Lead Data Scientist.

Requirements

  • A master’s degree or Ph.D. in Bioinformatics, Statistics, Computational Biology, Computer Science, Bioengineering, or a related quantitative discipline.
  • At least three years of professional experience with high-throughput flow cytometry data in a regulated, academic, or biotech setting.
  • Fluency in Python and experience with NumPy, Pandas, SciPy, scikit-learn, and PyTorch.
  • Demonstrated use of software engineering practices, including Git, CI/CD, workflow managers such as Nextflow, Docker environment pinning, and ML artifact tracking.
  • Familiarity with flow cytometry feature generation and batch-effect harmonisation.
  • Familiarity with cross-validation leakage risks in high-dimensional flow cytometry predictive modelling.
  • Existing right to work in the UK.

Benefits

  • Competitive salary and company bonus scheme.
  • Pension.
  • Health insurance.
  • Life assurance.
  • Income protection.
  • A fast-paced, collaborative startup environment.
  • Opportunities for career development and progression.

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