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About IMU Biosciences IMU Biosciences has developed proprietary platform technologies that generate and translate vast system-level immune data into actionable insights and tools to drive the development of precision medicines across a variety of diseases. IMU leverages advanced immune profiling with proprietary AI and machine learning analytics to uncover novel clinical immune signatures. IMU continues to establish partnerships with leading pharma and biotech companies to advance disease diagnosis, optimise product selection, and improve patient stratification and monitoring - while also building its own pipeline of innovative products. About The Role We are seeking a Bioinformatics Software Engineer to drive the flow cytometry data engineering infrastructure, annotation pipelines, model registry operations, and code governance for an advanced clinical biomarker discovery platform operating under machine learning industry best practices and Medical Device Machine Learning (MedDev ML) standards. In this role, you will be responsible for translating high-dimensional flow cytometry data into reproducible, production-grade features compliant with industry best practices and MedDev ML framework requirements. Your key deliverables will include frozen, reproducible feature pipelines and locked model registry artifacts. You will oversee automated cell-type annotation, manage model artifact registries, enforce data integrity, trace compliance, and handle leakage controls across machine learning partitions while leading peer code reviews. Responsibilities Flow Cytometry Data Engineering & Annotation Pipelines: Execute automated cell annotation workflows on large-scale cytometry datasets; generate cell membership files, cell subset ratio tables, and automated outlier quality-control (QC) reports. Apply locked automated gating ensembles across cross-validation and evaluation partitions, tracking and recording per-patient gating exposure metrics. Establish and lock baseline models and numerical stability criteria for flow cytometry feature extraction pipelines. Model Registry & Governance: Manage the formal model lifecycle registry—versioning, freezing, and locking gating ensembles and candidate predictive models in adherence with industry best practices and MedDev ML regulatory frameworks. Refit shortlisted machine learning models on expanded cohorts and execute final model freezing prior to blinded clinical evaluations. Ensure full reproducibility by committing versioned code, pinning environment specifications (Conda/Docker), and logging all random seeds and model hyperparameters. Code Quality, Review & Data Integrity: Serve as a primary code reviewer across the computational pipeline, enforcing strict data leakage controls, data integrity checks, and modular coding standards. Participate in Independent Method Reviews (IMRs), traceability audits, and regulatory compliance verification under industry best practices and MedDev ML guidelines while interfacing closely with Machine Learning Engineers, Bio-statisticians, and Clinical Scientists. Team and Ways of Working Embedded in a multidisciplinary team of 4 (ML Engineers, Biostatisticians, and Clinical Scientists) reporting directly to the Lead Data Scientist. 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. Technical Must-Haves (Essential Skills) Programming Mastery: Fluent in Python (NumPy, Pandas, SciPy, scikit-learn, PyTorch). Software Engineering & Version Control: Proven experience with software engineering best practices: Git version control, CI/CD, workflow managers (Nextflow), environment pinning (Docker), and ML artifact tracking. Cytometry Knowledge: Familiarity with flow cytometry feature generation and batch-effect harmonisation.

Bioinformatics Software Engineer

Don't apply unprepared

Bioinformatics Software Engineer

Don't apply unprepared