At, our mission is to accelerate the shift to Machine Learning in a way that maximizes participation and benefit to people while maintaining trust. We recently released PowerFlow, an end-to-end federated learning platform that enables data scientists to train machine learning models across distributed data silos, while seamlessly managing privacy, security, and regulatory risks. Our vision is to unlock new collaboration opportunities in industries such as healthcare and finance, to help more people with their critical needs and close the health, wealth, and wellness gaps.

We are looking for a Machine Learning Scientist (Privacy) to join our research team. This role delves into the art of the possible, and is neatly placed at the cross-section of cutting edge science and latest industry trends. It also represents a unique opportunity for someone who wants to work with a team of smart and passionate individuals committed to bringing new AI/ML products to the business enterprise world. 


What you will be doing:

  • Researching and developing adversarial machine learning models to better understand different types of privacy threats, and establishing the evaluation framework for the privacy risk of machine learning models in practice.
  • Researching and developing privacy preserving methods to protect our machine learning solutions against the privacy risk, and add privacy preserving capabilities to our platform/product.
  • Conduct experiments that focus on reproducibility and reliability to test out complex ideas.
  • Present and share research findings in a clear manner through’s internal technical reports (and when appropriate, share these findings through external outlets such as paper publications and conference presentations).

You have:

  • A Masters or PhD in Machine Learning, Applied Statistics, Computer Science, or a related quantitative discipline. 
  • 5+ years of hands-on machine learning experience.
  • Expertise in at least one of the following areas: Adversarial machine learning, Cryptographic Approaches (e.g. homomorphic encryption), Perturbation Approaches (e.g. differential privacy).
  • High quality research publications and/or open-source projects.
  • Great proficiency in machine learning tools such as TensorFlow, PyTorch, Scikit Learn, Theano, Spark MLlib.


The diverse experiences, ideas, and identities of’s team members help us make better decisions and drive great results. We foster an inclusive work environment that welcomes team members of all backgrounds and perspectives. We are committed to providing a meaningful environment for every member of our team. We hire exceptional people and reward them with trust, autonomy, mentorship, and growth.

We are naturally curious and have strong attention to detail. We love working in a team environment where trust is key and we all strive to make an impact every day. If this sounds like the right fit, please apply and come work with us.

Apply for this Job

* Required