Federated & Private Machine Learning Research Internship

Owkin is hiring!



Owkin is a French-American startup that uses AI and machine learning to augment medical and biology research. Its proprietary platform, Owkin Socrates, uses machine learning technology to integrate biomedical images, genomics and clinical data to discover biomarkers and mechanisms associated with diseases and treatment outcomes. The company develops scientific collaborations with top-tier medical institutions and partners with leading pharmaceutical companies. Owkin has developed a state-of-the-art federated learning technology in healthcare to overcome the sharing problems associated with medical data, building collective intelligence from distributed data at scale while preserving privacy and security.

Our data scientists are among the best in the world, with several Kaggle Masters, DREAM Challenge top performers, and publications in ICML, NIPS and other top scientific journals. Owkin has raised a total of 18.1M$ with funds including Google Ventures and Otium capital. To build this vision, Owkin is growing rapidly, with offices located in the USA, France, UK.

Job Description

The federated learning research (FLR) group is looking for a promising research intern to focus on topics in the field of privacy-preserving ML (PPML). From cryptographic techniques to differential privacy, the application of privacy-enhancing techniques to distributed machine learning is core to Owkin’s mission to accelerate medical research while respecting patient privacy. This internship provides a unique opportunity to study a fast-growing and important field, while also seeing your work used for good on real-world data.

During this internship, you will collaborate closely with, and receive mentorship from, the other members of the FLR group while conducting primary research. The successful intern will be expected to contribute to research review, conduct experimental validations of their topic of study, and report their detailed findings to the group and Lab.

The FLR group works on all research aspects related to distributed and privacy-aware machine learning, with a strong focus on the design, validation, and benchmarking of federated strategies, optimization of orchestration methodologies, as well as privacy enhancing techniques. The group also helps Owkin’s data scientists design novel privacy-aware distributed algorithms for particular multi-center projects.

Preferred Experience

We are looking for someone:

  • Currently finishing an MS in machine learning or an associated field and looking continue their research career
  • Motivation to work at the intersection of healthcare and AI
  • Excellence in communication and technical writing
  • Experience in Linux/Unix-like environments and Python
  • Experience with deep learning frameworks (Tensorflow, PyTorch, etc.)
  • Fluent in English

Pluses :

  • Participation in data science projects and competitions
  • Contribution to publications in conferences and journals (NeurIPS, ICML, ICLR, etc.)
  • Previous work or projects in the area of PPML
  • Contributions to open-source machine-learning or privacy-focused projects
  • Fluency with Git and basic software engineering patterns


  • Competitive remuneration
  • International and High-profile team
  • The whole startup package: regular activities, tech meetup, onsite events...
  • Very convenient work atmosphere and access to premium computing hardware

Recruitment Process

Please attach a CV and a cover-letter explaining your motivation and what you hope to study with this team. Applications without a cover letter will be automatically rejected.

Owkin is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, sex, gender, sexual orientation, age, color, religion, national origin, protected veteran status or on the basis of disability.

Additional Information

  • Contract Type: Internship (Between 6 and 6 months)
  • Start Date: 13 September 2020
  • Location: Paris, France (75010)
  • Education Level: Master's Degree
  • Experience: < 6 months
  • Occasional remote authorized