Algorithms Engineer, Differential Privacy

Oblivious

2 - 5 years

4 - 7 Lacs

bengaluru

Posted:7 hours ago| Platform:

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Skills Required

training health insurance statistical modeling machine learning database cryptography statistics sql python testing

Work Mode

Work from Office

Job Type

Full Time

Job Description

Responsibilities

Privacy Accounting & Mechanisms: Implement and analyse privacy loss accountants (RDP, zCDP) and their conversions to ( , )-DP. Calibrate and apply noise mechanisms (Gaussian, Laplace) based on rigorous sensitivity analysis.
Differentially Private SQL Engine: Develop algorithms for static and dynamic sensitivity analysis of relational operators. Build query rewriting logic to inject calibrated noise and manage a per-user privacy budget ledger.
Compiler & Static Analysis: Use Python AST manipulation and static analysis to enforce a DP-safe execution environment, ensuring user-submitted code cannot leak private information.
DP Synthetic Data: Implement and benchmark state-of-the-art algorithms (e.g., MWEM, PGM, PrivBayes variants) for high-dimensional synthetic data generation, analysing their privacy-utility trade-offs.

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.

Desirable

Direct experience with differential privacy concepts or libraries (OpenDP, SmartNoise, TensorFlow Privacy).
Knowledge of compiler design, abstract syntax trees (ASTs), or program analysis.
Experience with machine learning, particularly with noise models, statistical learning theory, or generative models.
Familiarity with SQL parsers or database internals.

Private Health Insurance
Paid Time Off
Work From Home, with one required in-office anchor week every six weeks for deep collaboration and planning
Training & Development

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