ML Engineer

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Arrondissement of BrusselsCapital, Brussels, Belgique Excelra Knowledge Solutions Pvt. Ltd. Temps plein

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pWe are hiring a Scientific Manager to take ownership of the machine learning and experimental design pipelines behind our customer’s genomic target prediction work. Much of this currently lives in individual scripts maintained by individual scientists. Your job is to turn it into an automated, version-controlled and reproducible platform the whole team can rely on – and to keep it scientifically sound by working shoulder to shoulder with our customer’s wet-lab and computational biology colleagues. /ppThis is a hands‑on engineering role in a scientific environment. You will spend more of your time building, automating and hardening pipelines than running individual analyses, so we are looking for someone who is as comfortable with Git, containers and workflow managers as with genomics data. /ppA PhD in bioscience engineering, bioinformatics, computer science or machine learning engineering – or an MSc with 5+ years of equivalent experience. You can demonstrate this through peer‑reviewed publications, contributions to public code repositories or relevant project work. /pulliStrong Python, and the habit of writing code that colleagues can read, test and re‑run. /liliProven experience taking analysis code into production: version control with Git, code review, packaging, containerization (Docker, Singularity/Apptainer) and automated testing or CI/CD. /liliHands‑on experience with workflow orchestration – Nextflow, Snakemake or equivalent. /liliExperience running workloads on cloud infrastructure (AWS) and/or HPC clusters (e.g. SLURM). /liliSolid grounding in statistical modelling and machine learning applied to biological data, including model evaluation and experiment tracking. /liliThe ability to translate between engineering and bench science, and to work with scientists who are not engineers. /liliYou are eligible to live and work in Belgium. /li /ulh3What will you do? /h3ulliAutomate and professionalize internal ML and experimental design pipelines for genomic target prediction. /liliMigrate existing scripts and notebooks into a production‑ready, version‑controlled codebase. /liliReduce the manual input required for dataset preparation, settings configuration and pipeline execution. /liliIntegrate pipelines into existing digital infrastructure, including AWS and HPC environments. /liliImplement version control, testing and reproducibility standards across ML workflows, and help the team adopt them. /liliCollaborate closely with wet‑lab and computational biology colleagues to make sure pipelines meet real scientific requirements. /liliRun several projects in parallel, (re)assigning tasks as priorities shift, and present results concisely to stakeholders inside and outside your domain. /li /ul

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