Data Privacy and Security Machine Learning Engineer

  • EPFL
  • Lausanne, VD, Switzerland
  • 23/05/2022
Full time Data Science Data Engineering Machine Learning Data Analytics Big Data Data Management Statistics

Job Description

The Machine Learning and Optimization lab of EPFL lead by Prof. Martin Jaggi performs research algorithms and applications of machine learning. Algorithms from our lab have become state-of-the-art for federated learning as well as communication efficient decentralized deep learning. Our developed open source software and algorithms have found numerous applications in industry and academic use. For example, an algorithm developed by our lab was recently integrated into PyTorch, the most prominent popular deep learning software, as the default method for communication compression for distributed training.

As one of the highest-ranked technology institutes internationally, EPFL offers a thriving intellectual environment and outstanding facilities. It is located in Lausanne, a beautiful and culturally active city on the shores of Lake Geneva close to the Swiss and French Alps.

Tune Insight SA (https://tuneinsight.com) is an EPFL startup that spun off from the EPFL Laboratory for Data Security. The mission of Tune Insight is to orchestrate sensitive data collaborations to carry out advanced analytics and machine learning leading to collective insights, with each organization remaining in full control of its own data. The used technology has been conceptualized and designed at the Laboratory for Data Security. Tune Insight brings this technology to production systems that can be deployed in the most demanding settings and environments.

The team has substantially contributed during the last four years to the HomomorphicEncryption.org international standardization effort, leading the definition of applications of secure computing technologies, and developing the most advanced state-of-the-art distributed homomorphic library (https://github.com/ldsec/lattigo), used by Amazon Homomorphic Encryption Toolkit, along with novel frameworks for the application of homomorphic encryption to machine learning on distributed and decentralized scenarios in telecommunications, logistics, financial networks, cyber threat intelligence, insurance, and healthcare.

Data Privacy and Security Machine Learning Engineer

Your mission :
As a hands-on technical specialist, the Data Privacy and Security Machine Learning Engineer will contribute to ongoing software developments of the Machine Learning and Optimization Laboratory, related to secure machine learning in highly sensitive application domains. The candidate will interact with our team and the joint projects with our industrial partners, notably the EPFL startup Tune Insight SA, and will work on applied practical systems implementing bleeding-edge secure machine learning for insight sharing in cybersecurity, financial services, and health in industry-oriented projects.
Main duties and responsibilities include :
  • Contribute to the software design, coding, testing, documentation and technical support of the state-of-the-art secure machine learning systems developed at the lab in collaboration with Tune Insight in federated and distributed environments
  • Contribute to the full-stack software design, implementation and testing of applied industrial projects, including secure machine learning in cyberdefense, insurance and health
  • Update job knowledge by tracking emerging security practices and standards (participating in educational opportunities, reading professional publications,...)
Your profile :
  • Master’s degree in Data Science, Computer Science or similar field; graduates from universities of applied sciences (HES) with appropriate experience will also be considered
  • Experience in programming and software engineering (full-stack development)
  • Experience in implementing and using machine learning and/or artificial intelligence algorithms
  • Knowledge of best practices for secure coding
  • Excellent oral/written communication in English, problem solving, strategic thinking and analytical skills
  • Knowledge of French is not required
Other valuable skills
  • Experience in privacy/data protection, deep learning, testing and privacy-focused design
  • Knowledge of information security and cryptographic protocols
  • Experience in database management
We offer :
Full-time position. Competitive salary and employment conditions, defined by EPFL regulations. Opportunities to continue this project in industry.

Start date :
Starting date as soon as possible.

To apply, please provide a motivation letter, a CV, 3 professional references, and evidence of your programming skills (e.g., link to GitHub).

For enquiries about this position, please contact Prof. Martin Jaggi (martin.jaggi@epfl.ch), Dr. Juan Troncoso-Pastoriza (juan@tuneinsight.com) or Prof. Jean-Pierre Hubaux (jean-pierre.hubaux@epfl.ch).

Term of employment :
Fixed-term (CDD)

Duration :
1 year, renewable

Remark :
Only candidates who applied through EPFL website or our partner Jobup’s website will be considered. Files sent by agencies without a mandate will not be taken into account