Privacy-Preserving ML Researchers

We are seeking Privacy-Preserving ML Researchers to help develop secure AI infrastructure for regulated industries. Focus areas include: * Federated learning * Differential privacy * Confidential inference * Secure multi-party computation * Privacy-preserving AI governance Responsibilities: * Research secure distributed AI architectures * Implement federated learning workflows * Build privacy-preserving inference systems * Evaluate adversarial and gradient leakage risks * Collaborate with cryptography and infrastructure teams * Contribute to production-grade confidential AI pipelines Required Skills: * Strong ML research background * Experience with PyTorch/JAX/TensorFlow * Familiarity with federated learning frameworks * Understanding of privacy attacks and mitigations * Strong mathematical and statistical foundations Understanding * Deep knowledge of NIST PQC standards (FIPS 203/204/205: ML-KEM, ML-DSA, SLH-DSA), hybrids, and crypto-agility. * Define integration patterns for Hardware Security Modules (HSMs) and on-premises/cloud PKI to support ML-KEM (Kyber), ML-DSA (Dilithium), and SLH‑DSA (SPHINCS+). * ML-DSA integration * Hybrid deployments (e.g., ML-KEM/ML-DSA with classical algorithms). * Strong background in lattice-based/hash-based cryptography, proficiency in C++/Python/Go; experience with liboqs or equivalent; understanding of performance/security trade-offs. Advanced degree in CS/Math/Cryptography highly desired. Preferred: * Publications/Understanding in NeurIPS, ICML, ICLR, PETS, or related venues * Experience with secure enclaves or TEEs * Differential privacy implementation experience