Frontend Engineer
Research Software Engineer for Translational Biomedical AI
Milancontract
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**Build the science that shapes the future of human health.
Application closing date: 15.09.2026**
Join a place where ambitious science thrives
Human Technopole is a rapidly expanding life science institute in Milan, where international researchers and cutting-edge technologies converge to accelerate biomedical discovery. Our mission is to transform bold scientific ideas into advances that improve human health.
Within this mission, the Health Data Science Centre develops advanced computational and machine learning approaches to analyse complex biomedical and clinical data. The Centre works at the interface of data science, machine learning, computational biology, epidemiology, and clinical research, with the goal of transforming research models into tools that can support biomedical discovery and healthcare innovation.
We are seeking a motivated
Research Software Engineer
to help translate research prototypes into robust, usable, and transferable software tools. We welcome candidates at different levels of seniority. Depending on the selected candidate’s experience, the position may be shaped as either a Junior or as a Senior Software Engineer.
This role sits at the interface between software engineering, machine learning and clinical translation. The successful candidate will work closely with data scientists, computational biologists, clinicians, ICT experts, and hospital partners to develop software systems that make advanced research models usable in real-world biomedical and healthcare settings. This position is funded by the European Union EU4H-2026-SANTE-PJ-04 – Project: European Cardiovascular Health data and AI Network (CHAIN), GA 101314833.
Your mission
As a Research Software Engineer for Translational Biomedical AI, you will work closely with data scientists, computational biologists, clinicians, ICT experts, and external collaborators to make biomedical machine learning models easier to use, test, and share across research environments.
You Will Help Researchers Turn Prototype Code, Trained Models, And Analysis Pipelines Into Software That Is More Maintainable, Reproducible, Documented, And Portable. You Will Contribute To Software Tools That Can
- Transform research code into maintainable and documented software.
- Package trained models and inference pipelines for deployment in external organizations.
- Enable hospitals and collaborators to test models locally on their own data without transferring sensitive patient-level information.
- Support reproducible model evaluation across sites.
- Provide usable interfaces, APIs, dashboards, or command-line tools depending on project needs;
- ensure that software tools are robust, secure, documented, and maintainable.
The role is not primarily a machine learning research position, but the candidate should have enough understanding of machine learning workflows to work effectively with researchers developing predictive, generative, and analytical models for biomedical and clinical data.
Grow Your Skills
You will enhance your professional skills by contributing to:
Research software engineering for biomedical AI
- Refactoring and modularising scientific Python code.
- Turning research prototypes into robust, reusable, and documented software.
- Supporting reproducible model training, validation, inference, and reporting.
- Working with researchers to translate scientific requirements into software tools.
- Developing tools that allow researchers, clinicians, and collaborators to interact with biomedical AI models in a controlled and usable way.
Machine learning model packaging and deployment
- Packaging trained models, preprocessing pipelines, configuration files, metadata, and evaluation scripts into portable software artifacts.
- Developing containerized model environments using Docker and related technologies.
- Building reproducible inference pipelines that can be transferred to hospitals or external research organizations.
- Supporting privacy-aware validation scenarios where models are tested locally without transferring sensitive data.
- Build CICD pipelines to support the software and AI models development life cycle, from development to testing and deployment.
Human Technopole supports career development through training, mentoring, and dedicated learning opportunities.
What You’ll Bring
Essential
- Degree in Computer Science, Software Engineering, Data Science, Bioinformatics, or equivalent professional experience.
- Experience in software development, research software engineering, machine learning engineering, data science, or similar roles.
- Strong programming skills in Python.
- Familiarity with scientific or machine learning Python libraries, such as NumPy, pandas, scikit-learn, PyTorch, TensorFlow, or similar.
- Experience writing modular, maintainable, and documented code.
- Familiarity with Git and collaborative software development practices.
- Familiarity with machine learning workflows, including preprocessing, model inference, model evaluation, reproducibility, and experiment configuration.
- Experience packaging software, models, or computational workflows for reuse by other users.
- Familiarity with containerization technologies such as Docker.
- Good English communication skills, both written and spoken.
Preferred
- Experience with MLOps, model packaging, or deployment of machine learning models in research, clinical, or semi-production environments.
- Experience with automated testing and simple CI workflows.
- Experience developing APIs, command-line tools, dashboards, or lightweight web interfaces.
- Experience with workflow management tools such as Nextflow, Snakemake, Airflow, or similar.
- Familiarity with secure or privacy-aware analysis workflows.
- Familiarity with biomedical data, longitudinal clinical data, EHRs, or hospital research environments.
- Experience with cloud, institutional HPC, or hybrid computing environments.
- Familiarity with Kubernetes or production deployment environments.
- Experience contributing to open-source scientific software or collaborative research software projects.
Organizational and social skills
- Strong communication skills, given the multicultural and multidisciplinary nature of the team (including data scientists, biomedical researchers, clinicians, software engineers, and ICT experts).
- Ability to translate between research needs, clinical requirements, and software implementation.
- Strong problem-solving skills, a proactive approach and ability to work across research, technical, and clinical domains.
- Excellent teamwork skills.
- Ability to write clear technical documentation for both technical and non-technical users.
- Interest in biomedical research and in the translation of AI methods into tools that can support real-world health research.
- Ability to balance the flexibility required in research with the robustness required for usable software.
Why Human Technopole
Human Technopole offers an international and dynamic workplace, competitive welfare provisions, flexible working policies, and relocation support. Candidates moving to Italy may benefit from attractive tax benefits. We promote work–life balance and provide parental support initiatives.
This position offers the opportunity to work at the interface of advanced machine learning, biomedical research, software engineering, and clinical translation. The successful candidate will contribute to tools that help make complex AI models usable, testable, and transferable across research and healthcare settings.
Submit
How to apply
- CV.
- Motivation letter in English.
This is a 30-month fixed-term contract, offered under CCNL Chimico Farmaceutico, Level B2
Salary: up to € 50k, depending on the seniority of the candidate.
The position is based in Milan, Italy, within our vibrant international campus.
We strongly encourage applications from candidates belonging to protected categories (L. 68/99)
The Foundation reserves the right, at its sole discretion, to extend, suspend, modify, revoke, or cancel this job posting without giving rise to any rights or claims whatsoever in favor of the candidates; the Foundation reserves, however, the right not to proceed with the awarding of the above-described assignment due to the effect of supervening regulatory provisions and/or obstructive circumstances.
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