Machine Learning Engineer

Introduction

Help shape the future of healthcare AI, where innovation meets trust. At CERN, you will develop cutting-edge AI solutions that help prevent disease, improve prediction, and deliver trustworthy healthcare insights. You will work on technologies that make it possible to unlock the value of sensitive data without ever moving or exposing it, enabling collaboration where privacy is essential.

As part of this role, you will help develop and deploy CAFEIN®, CERN's federated AI platform powering major European healthcare initiatives, including UMBRELLA and AIM-SAFE. You will design advanced federated learning algorithms and secure distributed AI systems that allow models to learn from data across institutions while keeping the data exactly where it belongs.

Recognised as a lighthouse example of "data stays, AI collaborates", CAFEIN® combines state-of-the-art federated learning with CERN's unique strengths in trust, open science, GDPR compliance, and international collaboration. The result is a pioneering framework that is redefining how AI can advance healthcare while safeguarding privacy. Join us to build AI that learns globally, protects locally, and improves lives.

Functions

• Develop and test federated learning software and algorithms.
• Implement and evaluate privacy-preserving techniques, including differential privacy and homomorphic encryption.
• Develop tools to optimise data ingestions and assess the quality of data and images.
• Design and run machine-learning experiments using programming languages like Python with libraries as TensorFLow, PyTorch, or scikit-learn.
• Analyse data using statistical methods and visualisation for exploratory data analysis (EDA).
• Implement and benchmark state-of-the-art machine-learning methods, optimising federated learning for utility, communication efficiency, energy use and scalability.
• Design and deploy Internet of Things (IoT)-based federated learning architectures.

Qualifications

• Master's degree or equivalent relevant experience in the field of Machine Learning, Computer Science or Data Science or a related field.
• Experience:
  • Demonstrated experience developing and deploying federated learning solutions in real-world environments.
  • Proficiency in federated learning techniques, including aggregation algorithms, communication and network architecture, security and privacy features.
  • Hands on experience with network security, including certificates, encryption, access control, authorisation, and authentication.
  • Proven experience in coordinating or technically leading machine-learning deployments.
  • Excellent knowledge and hands on experience with software life-cycle tools and procedures (proficiency with Git and GitLab).
  • Excellent knowledge of programming techniques and languages: python, deep learning framework (e.g. TensorFlow, Keras, PyTorch, Jax or scikit-learn).
  • Experience in data analysis in a health care environment would be an asset.

Technical Competencies

• Knowledge and application of software life-cycle tools and procedures.
• Knowledge of programming techniques and languages.
• Testing, diagnosing and optimisation of software.
• Knowledge of best practices for developing secure software.
• Administration of computing systems, upgrades, application of security patches, system and data migrations, backup and recovery.

Behavioural Competencies

• Achieving Results: delivering high quality work on time and fulfilling expectations.
• Solving Problems: identifying, defining and assessing problems, taking action to address them.
• Working in Teams: cooperating constructively with others in the pursuit of team goals; balancing personal goals with team goals.
• Communicating Effectively: ensuring that information, procedures and decisions are appropriately documented.
• Learning and Sharing Knowledge: sharing knowledge and expertise freely and willingly with others; coaching others to ensure knowledge transfer.

Language Skills

Spoken and written English, with a commitment to learn French.

Eligibility and Closing Date

This vacancy will be filled as soon as possible, and applications should normally reach us no later than 12.12.2026 at 23:59 CET.

Employment Conditions

• Contract type: Limited duration contract (3 years).
• Working Hours: 40 hours per week
• Job Flexibility: Hybrid
• Job grade: 6-7
• Job reference: TE-DPS-AIM-2026-223-LD
• Field of work: Information Technologies
• Benchmark Job Title: Computing Engineer

Salary and Benefits

• A competitive salary (tax free), increasing in line with your years of relevant experience.
• 30 days of paid leave per year plus 2 weeks annual closure.
• Coverage by CERN's comprehensive health insurance scheme (for yourself, your spouse and children), and membership of the CERN Pension Fund.
• Family, child and infant monthly allowances depending on your individual circumstances.
• A relocation package (installation grant, removal, travel expenses) depending on your individual circumstances.
• Possibility to extend your contract up to 8 years + eligibility for indefinite contract tenure.