Privacy Engineer (AI/ML)

Location: San Francisco, California, United States. On-site.

Annual salary: $300,000–$350,000 USD, depending on background, skills, and experience.

Company

Thinking Machines Lab builds frontier AI models, model-development tools, and interfaces intended to extend human judgment and broaden human-AI communication.

What you will do

• Build and operate data governance systems covering classification, inventory, access, usage purposes, retention, deletion, and auditing.
• Implement isolation guarantees for customer training data brought to Tinker and user conversations with AI interfaces.
• Conduct privacy reviews and threat models for data sources, training runs, and product features, then ship the required controls, checks, or tooling.
• Translate GDPR, CCPA, and contractual requirements into policy-as-code, CI checks, and pipeline enforcement.
• Maintain shared privacy libraries and guardrails so private-by-default behavior is built into internal systems.

Requirements

• Experience shipping and operating at least one production privacy or data governance system, such as a retention or deletion pipeline, policy evaluation engine, data catalog or lineage system, consent system, or similar.
• Production coding experience in Python or a comparable language, with the ability to explain system behavior, failure modes, and debugging practices in detail.
• Direct experience enforcing privacy or governing data in an AI/ML pipeline involving training, fine-tuning, inference, or evaluation, including a specific privacy risk identified or mitigated.
• Experience choosing pragmatic privacy controls for real systems and explaining what was shipped as a result.
• Experience deriving a technical requirement or control from a legal or policy obligation such as GDPR or CCPA, and explaining both the obligation and the implemented control.

Nice to have

• Experience at an AI lab or with ML infrastructure, including training-data curation, multi-tenant fine-tuning platforms, or inference systems handling user data.
• Experience implementing a privacy-enhancing technique such as deidentification, differential privacy, or secure enclaves, including its limitations.
• Experience building data lineage, metadata, or policy-engine tooling, or large-scale deletion and retention systems.
• Background in security engineering or direct experience working with a security team on access-control implementation.
• Experience as one of the first engineers building a privacy function, with clear ownership of the systems personally built.

Culture & Benefits

• On-site work in San Francisco, California.
• Health, dental, and vision benefits.
• Unlimited paid time off and paid parental leave.
• Relocation support as needed.
• Visa sponsorship is available, subject to fit and successful visa processing.