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About the role
- Algorithms Engineer designing and implementing core components of differential privacy systems for Oblivious. Focused on translating mathematical theory into production-ready code and robust algorithms.
Responsibilities
- 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 and pension plan
- Paid time off
- Training & development
Job title
Job type
Experience level
Salary
Not specified
Degree requirement
No Education Requirement
Tech skills
Location requirements
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