Find ways to learn from useful signals without casually exposing the people behind them. You will test privacy methods against explicit adversaries.
Not open yet: Research program
This work starts when that stage arrives, so there is no application to submit today and we will not pretend otherwise. What is written below is what the role is for and what would make somebody right for it, published early on purpose so you can decide whether it is worth watching.
The work
Research differential privacy, federated learning, secure aggregation or related techniques where they fit the problem. Measure privacy-utility tradeoffs and leakage from updates or outputs. Work with cryptography and product teams on deployable guarantees and understandable consent.
The milestone
In your first 90 days, deliver a threat model, a reproduced baseline and a privacy evaluation with clearly stated assumptions.
Evidence
Bring rigorous privacy or ML research expertise. Explain why keeping raw data on a device does not by itself make a learning system private.
Evidence, not credentials. We are describing work you can point at, in whatever form it exists.
The exercise
Evaluate a proposed federated training scheme for update leakage, malicious clients and withdrawal after participation.