Bioinformatics Software Engineer

IMU Biosciences

London, United Kingdom

Hybrid

Who this role is best for

Geared toward bioinformatics professionals comfortable with flow cytometry data engineering and regulatory compliance, working in a multidisciplinary team in London.

Best fit for

Candidates with advanced quantitative degrees and experience in flow cytometry data pipelines
— “Master’s or Ph.D. in Bioinformatics, Statistics, Computational Biology, Computer Science, Bioengineering, or a related quantitative field.”
Individuals with a track record in managing ML model registries and enforcing data governance
— “Manage the formal model lifecycle registry—versioning, freezing, and locking gating ensembles and candidate predictive models.”
Professionals who can translate complex data workflows into reproducible features for clinical use
— “Translate high-dimensional flow cytometry data into reproducible, production-grade features compliant with industry best practices.”

Things to consider

This role demands adherence to strict regulatory standards like MedDev ML.
— “compliant with industry best practices and MedDev ML standards.”
Candidates must be prepared for a collaborative, startup-like environment.
— “Fast-paced, collaborative startup culture”

How to stand out

Highlight experience with automated cell-type annotation and model freezing in regulated environments.
— “Execute automated cell annotation workflows on large-scale cytometry datasets.”
Demonstrate mastery in Python with specific libraries like scikit-learn and PyTorch.
— “Fluent in Python (NumPy, Pandas, SciPy, scikit-learn, PyTorch).”
Showcase ability to document computational methods for regulatory audits.
— “documenting computational methods for regulatory audits and effectively communicating technical workflows.”
Emphasize experience with CI/CD and environment pinning using Docker or Conda.
— “environment pinning (Docker), and ML artifact tracking.”
Demonstrate understanding of cross-validation leakage in high-dimensional cytometry modeling.
— “Familiarity with cross-validation leakage traps in high-dimensional flow cytometry predictive modeling.”

Pace · Fast PacedCollaboration · HighAutonomy · MediumDecision Impact · Team

Derived from job-description analysis by Serendipath's career intelligence engine.

What success looks like

Translate high-dimensional flow cytometry data into reproducible, production-grade features
Manage the formal model lifecycle registry

Typical background

Master’s or Ph.D. in Bioinformatics, Statistics, Computational Biology, Computer Science, Bioengineering, or a related quantitative field

Skills & requirements

Required

Flow Cytometry Data EngineeringAnnotation PipelinesModel Registry OperationsCode GovernanceData IntegrityMachine LearningMeddev ML Standards

Preferred

Advanced Statistical MethodsIndependent Method Reviews (imrs)

Stack & domain

Flow Cytometry Data EngineeringAnnotation PipelinesModel Registry OperationsCode GovernanceFlow Cytometry DataReproducible FeaturesIndustry Best PracticesMeddev ML Framework RequirementsAutomated Cell-type AnnotationModel Artifact RegistriesData IntegrityLeakage ControlsMachine Learning PartitionsPeer Code ReviewsFlow Cytometry Feature Extraction PipelinesModel RegistryVersioningFreezingLockingRefittingFinal Model FreezingBlinded Clinical EvaluationsVersioned CodeEnvironment SpecificationsLogging Random SeedsModel HyperparametersData Leakage ControlsData Integrity ChecksModular Coding StandardsIndependent Method ReviewsTraceability AuditsRegulatory Compliance VerificationMeddev ML GuidelinesFlow CytometryMachine LearningAIBioinformaticsStatisticsComputational BiologyComputer ScienceBioengineeringTeamworkProblem-solvingCommunicationDocumentationRegulatory AuditsClinical Biomarker DiscoveryPrecision Medicine

About the role

Original posting from IMU Biosciences via Ashby

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.
Core 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.
Qualification Requirements
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.
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.
Familiarity with cross-validation leakage traps in high-dimensional flow cytometry predictive modeling.
Nice-to-Haves (Desirable Skills)
Experience working with clinical trial datasets, industry best practices, and MedDev ML regulations.
Experience in transplant immunology, oncology, or hematologic malignancies.
Key Competencies
Strong collaboration and communication skills, including documenting computational methods for regulatory audits and effectively communicating technical workflows to clinical scientists and cross-functional teams.
Why Join Us?
Competitive salary and company bonus scheme
Pension, health insurance, life assurance, and income protection
Fast-paced, collaborative startup culture
Strong opportunities for career development and progression

Source: IMU Biosciences careers (Ashby)