IMU Biosciences
IMU Biosciences
IMU Biosciences ir Londonā bāzēts biotehnoloģiju uzņēmums, kas izstrādā augstas dimensijas imunoloģijas tehnoloģijas klīniskajiem pētījumiem, diagnostikai un zāļu izstrādei. Tā multiomikas un augstas caurlaidības platforma analizē vairāk nekā 100 miljonus imūno datu punktu no 2 ml asins parauga un reāllaikā izšķir aptuveni 2500 imūno šūnu apakštipu. Iegūtā populācijas mēroga imūno datu kopa atbalsta tādas jomas kā prognozējoša hematopoētisko cilmes šūnu transplantācijas skrīninga veikšana, atgrūšanas reakcijas novērtēšana cieto orgānu transplantācijā, kā arī ar atbildes reakciju uz ārstēšanu un nevēlamiem notikumiem saistītu imūno parakstu noteikšana imūnonkoloģijā. IMU Biosciences sadarbojas ar klīnisko pētījumu grupām, farmācijas un biotehnoloģiju uzņēmumiem, kā arī diagnostikas partneriem, lai imūnfenotipēšanu pārvērstu praktiskos klīniskos un komerciālos rīkos.

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.

Apraksts

  • 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.

Prasības

  • 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.

Priekšrocības

  • 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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