AI/ML Engineer

About Routex BioSys

Routex BioSys is an early stage R&D biotech that combines targeted molecular measurements with AI/ML to turn complex biological data into clear, interpretable insight for researchers. We start in oncology and cancer biology. Our products are for research use only (RUO).

Our team brings together AI/ML, bioinformatics, biomedical engineering and molecular biology.

We are hiring an AI/ML Engineer to build the models at the core of our analysis platform.

You will own them end to end, from data curation to validation and integration, and work daily with molecular biologists and bioinformaticians. Interpretability and reproducibility are core parts of the job, and you will grow into them with our team.

What You'll Do

• Design, train and validate ML models that turn laboratory measurements into biologically meaningful outputs.
• Build data processing and quality control pipelines for experimental data.
• Make model outputs explainable and report how far each result can be trusted.
• Build reproducible training and evaluation workflows with clear benchmarks.
• Work with wet-lab scientists on validation experiments and turn results into testable hypotheses.
• Integrate the models with the software team and follow the literature on interpretable ML and omics modeling.

Required Experience and Skills

• BSc grad or MSc/PhD (student) Computer Science, Bioinformatics, Computational Biology, Biomedical Engineering or a related field, or equivalent practical experience.
• Hands-on experience building and evaluating ML or bioinformatics models.
• Python and a deep learning framework (PyTorch or TensorFlow), ideally with scikit-learn, pandas and NumPy.
• Supervised and unsupervised ML on high-dimensional expression data with sound validation: cross-validation, feature selection or reduction, leakage control, and a habit of questioning what a model has actually learned.
• Hands-on experience with gene expression data and multiomics data (at least one of RNA-seq, microarray or qPCR).
• Working knowledge of molecular biology and the ability to work closely with wet-lab scientists.
• Good engineering practice: Git, testing, reproducible code and clear documentation.
• Clear communication in English with both ML and biology colleagues.

Nice to Have

• Pathway databases or pathway analysis tools.
• Model interpretability methods such as SHAP, and uncertainty quantification or calibrated uncertainty estimation.
• Regularized models and tree ensembles on small-sample, high-dimensional data.
• Analysis of experimental assay data, or wet-lab experience.
• Experience modeling public cancer datasets.
• Cloud compute (AWS or GCP), containers, workflow tools (Nextflow, Snakemake) and experiment tracking (MLflow, W&B).
• R.
• Background in oncology or cancer biology.
• Familiarity with transformers, foundation models or LLMs and transfer learning from large omics datasets.
• Peer-reviewed publications or open-source contributions.
• Experience in an early-stage startup or a multidisciplinary research group.

What We Offer

• A founding-team role with real ownership of the core models and technical direction.
• Work on a problem with clear scientific value: making molecular measurement more informative for cancer research.
• Daily collaboration with molecular biologists, bioinformaticians and engineers.
• Support for conferences, publications and continuous learning.
• Milestone-based equity/vesting may follow based on role, contribution, and mutually agreed milestones.
• Target pathway to a paid role.

How to Apply

Apply through LinkedIn. Send your CV to [email protected] with the subject line "AI/ML Engineer".