Bioinformatics Software Engineer

IMU Biosciences

. Execute automated cell annotation workflows on large-scale cytometry datasets .

Posted 10/12/2026full-timeLondon • United KingdomMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in executing automated cell annotation workflows and managing the model lifecycle for flow cytometry datasets. Proficient in Python and familiar with software engineering best practices, ensuring data integrity and compliance in regulated environments.

Highest-signal resume keywords

Flow Cytometry Data HandlingPython ProgrammingMachine Learning Model Lifecycle ManagementSoftware Engineering Best PracticesRegulatory Compliance Verification

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Automated Cell Annotation WorkflowsFlow Cytometry Feature ExtractionCross-Validation TechniquesData Integrity ChecksModel FreezingVersion Control with GitCI/CD PracticesBatch-Effect HarmonisationMachine Learning with PyTorchStatistical Analysis

Soft Skills

CollaborationCode ReviewCommunication

Tools & Technologies

NumPyPandasSciPyScikit-learnDockerNextflow

Industry Keywords

BioinformaticsComputational BiologyBiotechAcademic ResearchHigh-Throughput Data

Tech Stack

Tools & technologies

DockerNumpyPandasPythonPyTorchScikit-Learn

About the role

Key responsibilities & impact

Execute automated cell annotation workflows on large-scale cytometry datasets
Generate cell membership files, cell subset ratio tables, and automated outlier quality-control reports
Apply locked automated gating ensembles across cross-validation and evaluation partitions
Track and record per-patient gating exposure metrics
Establish and lock baseline models and numerical stability criteria for flow cytometry feature extraction pipelines
Manage the formal model lifecycle registry, including versioning, freezing, and locking gating ensembles and candidate predictive models
Refit shortlisted machine learning models on expanded cohorts and execute final model freezing before blinded clinical evaluations
Commit versioned code, pin Conda/Docker environment specifications, and log random seeds and model hyperparameters
Serve as a primary code reviewer across the computational pipeline
Enforce data leakage controls, data integrity checks, and modular coding standards
Participate in Independent Method Reviews, traceability audits, and regulatory compliance verification
Interface with Machine Learning Engineers, Biostatisticians, and Clinical Scientists
Report directly to the Lead Data Scientist

Requirements

What you’ll need

Master’s or Ph.D. in Bioinformatics, Statistics, Computational Biology, Computer Science, Bioengineering, or a related quantitative field
3+ years of professional experience handling flow cytometry high-throughput data in regulated, academic, or biotech settings
Fluent in Python, including NumPy, Pandas, SciPy, scikit-learn, and PyTorch
Proven experience with software engineering best practices, Git version control, 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 traps in high-dimensional flow cytometry predictive modeling
Right to work in the UK

Benefits

Comp & perks

Competitive salary and company bonus scheme
Pension
Health insurance
Life assurance
Income protection
Fast-paced, collaborative startup culture
Strong opportunities for career development and progression