Engineering Manager, Safeguards Data Infrastructure
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What the team is looking for.
About the role
Anthropic's Safeguards team is responsible for the systems that allow us to deploy powerful AI models responsibly , and the data infrastructure underneath those systems is foundational to getting that right. The Safeguards Data Infrastructure team owns the offline data stack that underpins our safeguards work: the storage layer for sensitive user data, the tooling built on top of it, and the interfaces that let the rest of the Safeguards organization access that data safely and ergonomically.
As Engineering Manager of this team, you'll be responsible for ensuring full portability of our safeguards data stack across an expanding set of deployment environments, building privacy-preserving data interfaces that enable ML and training workflows, and driving compliance with data regulations including HIPAA. This is a role at the intersection of infrastructure engineering, data privacy, and enterprise product requirements , and it sits at a critical juncture as Anthropic scales into new cloud environments and geographies.
Key responsibilities:
- Lead and grow a team of engineers delivering the data infrastructure and tooling that powers Anthropic's safeguards capabilities
- 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
- Build and maintain privacy-safe data APIs and interfaces that enable ML and training workflows while respecting data retention and access constraints
- Drive tooling and architecture decisions that maximize data retention within the bounds of our privacy and compliance requirements
- Manage privacy incident response processes and partner with compliance teams on regulatory requirements (e.g. HIPAA, EU privacy regulations)
- Collaborate closely with enterprise customers and product teams on zero data retention offerings, working balancing safety needs with robust enterprise data contracts
- Independently own and drive multiple workstreams, including planning, execution, and cross-team coordination
- Coach, mentor, and support the career development of your direct reports, helping them set and achieve their professional goals
- Partner with recruiting to attract, hire, and retain strong engineering talent
Minimum qualifications:
- Have 3+ years of front-line engineering management experience
- Have hands-on software engineering experience as an individual contributor prior to moving into management
- Are comfortable driving technical decisions in an ambiguous, fast-moving environment with competing priorities
- Have experience working cross-functionally across infrastructure, product, and compliance or security teams
- Are clear and persuasive communicators, both in writing and in person
Preferred qualifications:
- Have a track record of leading teams that build and operate data infrastructure at scale
- Have experience with multi-cloud or multi-region data portability, particularly in regulated environments
- Have built privacy-preserving data pipelines or interfaces for ML workloads
- Have experience with enterprise data contracts or zero data retention architectures
- Have explored novel approaches to data processing under strict access constraints, such as in-memory storage and compute for sensitive data
- Have a strong understanding of data privacy principles, PII handling, and compliance frameworks
- Have a passion for building diverse and inclusive teams
Logistics
- Minimum education: Bachelor’s degree or an equivalent combination of education, training, and/or experience
- Required field of study: A field relevant to the role as demonstrated through coursework, training, or professional experience
- Minimum years of experience: Years of experience required will correlate with the internal job level requirements for the position
- Location-based hybrid policy: Currently, we expect all staff to be in one of our offices at least 25% of the time. However, some roles may require more time in our offices.
- Visa sponsorship: We do sponsor visas! However, we aren't able to successfully sponsor visas for every role and every candidate. But if we make you an offer, we will make every reasonable effort to get you a visa, and we retain an immigration lawyer to help with this.
How we're different
We believe that the highest-impact AI research will be big science. At Anthropic we work as a single cohesive team on just a few large-scale research efforts. And we value impact , advancing our long-term goals of steerable, trustworthy AI , rather than work on smaller and more specific puzzles. We view AI research as an empirical science, which has as much in common with physics and biology as with traditional efforts in computer science. We're an extremely collaborative group, and we host frequent research discussions to ensure that we are pursuing the highest-impact work at any given time. As such, we greatly value communication skills.
The easiest way to understand our research directions is to read our recent research. This research continues many of the directions our team worked on prior to Anthropic, including: GPT-3, Circuit-Based Interpretability, Multimodal Neurons, Scaling Laws, AI & Compute, Concrete Problems in AI Safety, and Learning from Human Preferences.
Come work with us!
Anthropic is a public benefit corporation headquartered in San Francisco. We offer competitive compensation and benefits, optional equity donation matching, generous vacation and parental leave, flexible working hours, and a lovely office space in which to collaborate with colleagues. Guidance on Candidates' AI Usage: Learn about our policy for using AI in our application process.
- software engineering
- data infrastructure
- data privacy
- compliance
- HIPAA
- EU privacy regulations
- enterprise data contracts
- zero data retention architectures
- in-memory storage
- compute
- sensitive data
- data processing
- privacy incident response
- cross-functional collaboration
- team leadership
- communication skills
- multi-cloud data portability
- multi-region data portability
- privacy-preserving data pipelines
- ML workloads
- novel approaches to data processing
- data privacy principles
- PII handling
- compliance frameworks
- diverse and inclusive teams
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