Role overview
The work.
Meramia's knowledge work already lives inside boundaries the client is held to. This role makes those boundaries measurable: privacy-enhancing techniques on the training and inference path, red-team tests for leakage, and tools the rest of the lab can run without a paper as a crutch.
You will turn a result from the literature into code that ships, and you will say when a privacy guarantee is real and when it is a slide.
01
What you will do
- Prototype and land privacy-enhancing algorithms — differential privacy, secure aggregation, federated learning — on the training and inference stacks we actually run.
- Red-team the models for membership inference, inversion, and training-data memorisation. Write the failure down before someone else finds it.
- Build the evaluation suites and diagnostic libraries ML engineers can use across a model's life, not a one-off notebook.
- Sit with research, platform, security, legal and product so a regulation or a security principle becomes a guardrail in code.
- Investigate the privacy–utility trade: capability, latency and cost against a guarantee you can state.
02
What you bring
- PyTorch or JAX, and research-grade Python you will test rather than demo.
- Differential privacy (including DP-SGD), secure multiparty computation, or federated learning — implemented, not only cited.
- The attack surface: extraction, membership inference, poisoning — and how you measured it.
- A paper you can turn into a well-tested module without losing the claim that made the paper worth reading.
- The ability to explain a mathematical privacy guarantee to an engineer and to a policy lead in the same week.
03
Useful, not required
- Peer-reviewed work or open-source in privacy, security, cryptography or machine learning — NeurIPS, ICLR, USENIX Security, IEEE S&P, or the equivalent venue.
- PETs on a distributed train or a high-throughput inference path, not only on a single node.
04
What to send
- A CV and the two profile links the form asks for.
- One privacy or leakage result you implemented — the threat, the metric, and the trade you accepted.
05
The first quarter
- Audit the privacy evaluations and training workflows already in use.
- Ship one automated evaluation or differential-privacy module into the internal ML path.
- Write the privacy–utility result for the architectures we serve, with a deployable recommendation.