IMU Biosciences
IMU Biosciences
IMU Biosciences yra Londone įsikūrusi biotechnologijų įmonė, kurianti didelės dimensijos imunologijos technologijas klinikiniams tyrimams, diagnostikai ir vaistų kūrimui. Jos daugiomikė ir didelio našumo platforma iš 2 ml kraujo mėginio išanalizuoja daugiau kaip 100 milijonų imuninių duomenų taškų ir realiuoju laiku išskiria maždaug 2 500 imuninių ląstelių pogrupių. Gautas populiacijos masto imuninių duomenų rinkinys padeda taikyti tokias sritis kaip prognozinė kraujodaros kamieninių ląstelių transplantacijos patikra, persodintų solidinių organų atmetimo vertinimas ir su atsaku į gydymą bei nepageidaujamais reiškiniais susijusių imuninių požymių nustatymas imuno-onkologijoje. IMU Biosciences bendradarbiauja su klinikinių tyrimų grupėmis, farmacijos ir biotechnologijų įmonėmis bei diagnostikos partneriais, kad imunofenotipavimą paverstų praktiniais klinikiniais ir komerciniais įrankiais.

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.

Aprašymas

  • 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.

Reikalavimai

  • 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.

Privalumai

  • 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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