Senior Engineer/Leader – Rust

Our mission is to protect people’s privacy by preventing data breaches and surveillance.

Following a recent breakthrough in homomorphic encryption, we are now building a deep learning framework that enables fast and accurate inference over encrypted data, 1000x faster than currently published methods.

We believe privacy-enabling technologies should benefit the widest possible community of developers and security researchers, which is why everything we create will be published and open-sourced.

Zama is founded by Dr Rand Hindi, an serial AI entrepreneur, and Dr Pascal Paillier, who invented some of the most widely used homomorphic encryption schemes. 


Zama is tackling the problem space around the rampant reality of data breaches and surveillance. Currently, companies have no way to protect against unauthorized access to their user data, such as when hackers steal the data or governments subpoenas them. The massive financial losses incurred cannot be prevented with traditional IT security anymore, as hackers have an incentive to hack a company that is proportional to their userbase. This is why the market for data security is growing 20% YoY.

What is being proposed is to use homomorphic encryption to keep the data encrypted server-side while it is being processed. From the user’s perspective, it’s the exact same service. However, the company no longer has access to the data, and so it cannot be stolen. This solves both privacy for users and security for companies.

Conversations have shown demand from companies offering machine-learning services in the cloud, such as facial recognition or speech APIs, as they currently have no way to protect against motivated hackers and governments. By implementing Zama, MLaaS companies would now be able to offer their service encrypted, thereby no longer having to worry about data breaches, localization or privacy.


Our machine learning solution is composed of 3 central elements: 

  • a cryptographic SDK that handles all the cryptographic operations
  • a homomorphic compiler that converts a trained neural network model into an encrypted equivalent
  • a deep learning inference engine that runs the model generated by the compiler, to produce encrypted output from encrypted inputs

Open source by design, we are building the product in a way that maximizes adoption by developers, while making our framework so easy to integrate that no cryptography knowledge would be required to implement it in production.


Your team (and thus you) will be responsible for:

  • turning cryptography research code into product code
  • designing, implementing and optimizing the various components of our product, making it as easy to integrate as possible
  • creating robust testing frameworks against various attacks
  • managing our open source repositories and the community around it


  • experience implementing high performance mathematical code
  • experience using Rust in a production environment
  • optionally experience implementing deep learning and/or cryptography
  • be based in or willing to relocate to Paris, France
  • passionate about privacy and open source