Design, implement and evaluate privacy-preserving algorithms.
Implement and integrate various differential privacy mechanisms.
Calibrate and apply noise mechanisms (Gaussian, Laplace) based on rigorous sensitivity analysis.
Use Python AST manipulation and static analysis to enforce a DP-safe execution environment, ensuring user-submitted code cannot leak private information.
Privacy Accounting & Mechanisms: Analyse and implement privacy accounting and mechanisms.
DP Synthetic Data: Implement and benchmark state-of-the-art algorithms for high-dimensional synthetic data generation.
Collaborate with cross-functional teams to design, develop, and deploy privacy-preserving systems.
Requirements
Strong foundation in probability, statistics, and linear algebra. You must be comfortable with statistical modelling, proving bounds, and reasoning about error/variance.
Proficiency in Python for scientific computing, including numerical stability considerations (e.g., floating-point precision, clipping, scaling).
Demonstrated ability to translate mathematical concepts from academic papers or technical specifications into robust, well-tested code.
Excellent knowledge of compiler engineering, ideally having built a simple compiler in the past.
Experience with data structures and ASTs.
**Desirable**
Experience in designing and implementing privacy-preserving algorithms
Experience with machine learning, particularly with noise models, statistical learning theory, or generative models.
Familiarity with SQL parsers or database internals.
Benefits
Private Health Insurance
Paid Time Off
Work From Home, with one required in-office anchor week every six weeks for deep collaboration and planning
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