> Markdown version of [/jobs/ext/4083420-machine-learning-ml-engineer-ai-bioinformatics](https://www.wearedevelopers.com/jobs/ext/4083420-machine-learning-ml-engineer-ai-bioinformatics). Every page supports `.md` or `Accept: text/markdown`. Links point to the HTML versions so they work for humans too. Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Machine Learning (ML) Engineer - AI & Bioinformatics - **Company:** ProQR Therapeutics - **Location:** Leiden, Netherlands - **Experience:** Experienced - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Data Analysis, Bioinformatics, Command-Line Interface, Information Engineering, Data Infrastructure, Data Integration, Experimental Data, Python (Programming Language), Linux System Administration, Machine Learning, Metadata Standards, Performance Tuning, Software Construction, Data Streaming, Data Processing, Scripting, Pytorch, Deep Learning, Generative AI, Git, Containerization, Information Technology, Software Version Control, Data Pipelines, Docker - **Published:** October 8, 2026 - **Apply:** https://www.adzuna.nl/details/5919825146 ## About the Role * MSc in Computer Science, Data Science, or a related quantitative discipline * At least 2 years of relevant work experience in Machine learning, Generative AI models, Deep learning, or Data engineering (industry or advanced academic research) * Strong proficiency in Python for data processing, scripting, and model development * Practical experience with JAX or PyTorch for training and evaluating machine learning models * Basic understanding of molecular biology plus willingness to learn RNA editing and oligonucleotide concepts on the job * Hands-on experience with Linux-based environments and command-line tool. * Familiarity with Docker (strongly preferred) or similar containerization tools for reproducible environments * Demonstrated experience or interest in handling biological datasets Knowledge of software engineering best practices, including version control (Git), modular code design, and documentation * Exposure to R for statistical workflows * Curiosity and adaptability to explore the best tools and approaches for each project Preferred: * Experience with foundation models, such as pre-training, fine-tuning, or applying large sequence models (biological or otherwise) * Experience with active learning, Bayesian optimization, or other sequential experimental design methods, * Analytical and accurate * Creative thinker * Flexible and adaptable * Hands-on mentality * Proactive and pragmatic self-starter, with a result-oriented mindset * Team player * Autonomous and coachable * Being able to work in a fast-paced environment * Being able to prioritize multiple tasks * Patient-oriented attitude * Excellent knowledge of spoken and written English * A nice person ## Description This position is ideal for someone with a strong foundation in machine learning and data engineering who is eager to apply those skills to real-world biological challenges and data-driven decision-making. You will work closely with laboratory and computational scientists, bioinformaticians and domain experts to ensure seamless integration of data and models., * Design editing oligonucleotide (EON) candidates using ML models in a lab-in-the-loop cycle: propose sequences for synthesis and testing, incorporate the new experimental results, and iteratively improve both the models and the designs * Develop, train, and evaluate AI models using JAX or PyTorch for biological prediction and design tasks in RNA therapeutics * Adapt and fine-tune foundation models for downstream tasks, and explore new architectures and methods from literature * Benchmark model performance across diverse biological tasks and contribute to model selection and improvement * Work with wet-lab teams to plan design-test-learn rounds, including selecting which candidates to test so each round is as informative as possible * Design, build, and maintain modular, reproducible data pipelines that transform raw experimental data into standardized, AI-ready datasets * Automate and optimize data flows to reduce manual steps and improve scalability, using scripting and containerized tools (e.g., Docker) * Collaborate cross-functionally with domain scientists, bioinformaticians, and project leads to align computational outputs with experimental context * Contribute to data infrastructure initiatives, including FAIR-compliant schema development, metadata standards, and platform integration * Support statistical analyses and data exploration using Python and/or R * Ensure reproducibility through modular code design, version control (Git), thorough documentation, and adherence to best practices * Engage in technical discussions, retrospectives, and team knowledge-sharing sessions to foster learning and continuous improvement