Engineering Manager, Safeguards Data Infrastructure

Anthropic Anthropic · AI Frontier · New York, NY · Safeguards (Trust & Safety)

Engineering Manager for Safeguards Data Infrastructure at Anthropic, focusing on building and managing the offline data stack that supports AI safeguards. Responsibilities include data portability, privacy-safe data APIs for ML/training, compliance with regulations like HIPAA, and managing a team of engineers.

What you'd actually do

  1. Lead and grow a team of engineers delivering the data infrastructure and tooling that powers Anthropic's safeguards capabilities
  2. Own the strategy and execution for porting the safeguards offline data stack — including PII storage and tooling — across new cloud and deployment environments as Anthropic expands
  3. Build and maintain privacy-safe data APIs and interfaces that enable ML and training workflows while respecting data retention and access constraints
  4. Drive tooling and architecture decisions that maximize data retention within the bounds of our privacy and compliance requirements
  5. Manage privacy incident response processes and partner with compliance teams on regulatory requirements (e.g. HIPAA, EU privacy regulations)

Skills

Required

  • 3+ years of front-line engineering management experience
  • Hands-on software engineering experience as an individual contributor prior to moving into management
  • Comfortable driving technical decisions in an ambiguous, fast-moving environment with competing priorities
  • Experience working cross-functionally across infrastructure, product, and compliance or security teams
  • Clear and persuasive communicators, both in writing and in person

Nice to have

  • Track record of leading teams that build and operate data infrastructure at scale
  • Experience with multi-cloud or multi-region data portability, particularly in regulated environments
  • Built privacy-preserving data pipelines or interfaces for ML workloads
  • Experience with enterprise data contracts or zero data retention architectures
  • Explored novel approaches to data processing under strict access constraints, such as in-memory storage and compute for sensitive data
  • Strong understanding of data privacy principles, PII handling, and compliance frameworks
  • Passion for building diverse and inclusive teams

What the JD emphasized

  • portability of our safeguards data stack across an expanding set of deployment environments
  • privacy-preserving data interfaces that enable ML and training workflows
  • driving compliance with data regulations including HIPAA
  • privacy and compliance requirements
  • HIPAA
  • EU privacy regulations
  • enterprise customers
  • zero data retention offerings
  • data retention and access constraints
  • multi-cloud or multi-region data portability, particularly in regulated environments
  • privacy-preserving data pipelines or interfaces for ML workloads
  • enterprise data contracts or zero data retention architectures