ML Research Graduate Intern (AI)

TL;DR

ML Research Graduate Intern (AI): Contributing to the development and deployment of large-scale clinical foundation models with an accent on training infrastructure, post-training algorithms, and data curation. Focus on solving complex challenges in RL, model evaluation, and inference optimization within a regulated clinical environment.

Location: Must be based in or able to work onsite in Amsterdam or Zurich

Salary: CHF4,000/EUR3,000 gross per month

Company

Clinical AI research lab developing frontier-class models deployed in leading European hospitals.

What you will do

• Collaborate with PhD researchers and senior engineers on core clinical foundation model development.
• Work on synthetic data generation, multimodal pipelines, and data quality filtering.
• Design and implement clinical benchmarks and agentic evaluation infrastructure.
• Contribute to pre-training, post-training algorithms, and distributed training at scale.
• Optimize inference and training stacks, including quantization and throughput tuning.
• Own specific research projects and present findings directly to the core research team.

Requirements

• MSc student or recent graduate in machine learning, computer science, or a related quantitative field.
• Strong programming skills in Python and experience with ML frameworks like PyTorch.
• Hands-on experience with ML research or engineering through coursework, thesis, or previous internships.
• Clear technical communication skills for design documentation and team collaboration.
• High agency and motivation to work on complex, real-world clinical AI problems.

Culture & Benefits

• Work at the frontier of clinical AI with experienced researchers and engineers.
• High level of ownership and direct impact on production systems.
• Active consideration for full-time roles upon successful internship completion.
• Collaborative environment focused on rapid growth and technical challenge.

Hiring process

• 30-minute screening call to discuss motivation and background.
• Technical assessment focused on a specific research area.
• Onsite or video interview to meet the team and align on project fit.