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Job Description
About the Role
Develop privacy-preserving generative systems enabling secure data sharing at SynGen Technologies. Design GAN architectures that replicate complex healthcare datasets with mathematical privacy guarantees while retaining analytical utility. Create the foundation for multi-party data collaboration across secured enclaves.
Responsibilities
- Architect generative models for time-series EHR data
- Establish differential privacy frameworks
- Develop synthetic data validation metrics
- Implement homomorphic encryption pipelines
- Optimize GPU cluster utilization for large-scale generation
- Design federated learning systems across synthetic nodes
- Collaborate with compliance teams on HIPAA certification
Requirements
- PhD ML focus on generative models/privacy
- Experience with multi-table relational synthesis
- Expertise in PyTorch/TensorFlow Privacy
- Knowledge of privacy budgets and composition theorems
- Published work in NeurIPS/PETS on private ML
- Experience implementing MPC/SHE schemes
- Familiarity with confidential computing environments
Benefits
- $50,000/year compute resource allowance
- Differential privacy research grants
- Dedicated high-memory GPU workstations
- Cryptography research collaborations
- Attend BlackHat/DEF CON as technical representative
- Wellness weeks with no connectivity
- Full tuition reimbursement for continuing education
About the Company
SynGen Technologies pioneers enterprise-grade synthetic data for regulated industries. Trusted by 15+ top hospitals and Mayo Clinic Partners, our privacy-first generative simulations enable breakthroughs while eliminating re-identification risks. Our Newton HQ features secure data enclaves certified under HITRUST CSF standards.
Job ID: enterprise-synthetic-data-architect-9S5U2
