Engineering Manager, Data Architecture

Details

Anthropic’s mission is to create reliable, interpretable, and steerable AI systems. We want AI to be safe and beneficial for our users and for society as a whole. Our team is a quickly growing group of committed researchers, engineers, policy experts, and business leaders working together to build beneficial AI systems.

As the Engineering Manager for Data Architecture, you will lead the team responsible for building our north star implementation of data infrastructure. This team owns the full lifecycle of Anthropic’s most critical data, ranging from customer-facing data like prompts and exchanges (highly PII) to core business data such as logging and financial records. Your mission is to grow this infrastructure into a world-class platform that powers our rapidly expanding business while maintaining the highest standards for AI safety and regulatory compliance.

This is a high-leverage leadership role where you will architect systems that support model training with customer consent, legal reviews, and AI safety evaluations (Safe Guard). You will ensure our data platform is inherently secure, massively scalable, and flexible enough to support diverse product surfaces across multiple cloud environments.

Responsibilities

• Build and lead the team: Recruit and mentor a team of world-class data and infrastructure engineers; establish the team’s technical vision, operational standards, and strategic roadmap.
• Drive technical strategy: Define the long-term architecture for Anthropic’s data stack, ensuring it supports high-velocity model training and complex inference workloads across all cloud regions.
• Architect scalable pipelines: Lead the design and implementation of robust, automated data pipelines that handle petabyte-scale datasets with high reliability and performance.
• Implement robust governance: Build the systems and processes for automated data discovery, lineage tracking, and lifecycle management to ensure high data quality and integrity.
• Security and compliance-by-design: Ensure data architecture inherently supports global privacy regulations and security requirements through automated controls and privacy-preserving architectures.
• Cross-functional enablement: Partner with ML, Product, and Legal teams to unlock the power of data, providing the tools and platforms needed to derive insights without compromising safety.
• Standardize data quality: Define and enforce SLAs for data availability and accuracy, building internal tools to monitor and maintain the health of the entire data ecosystem.
• Evangelize the data mission: Advocate for the importance of modern data architecture as a core component of AI safety, communicating progress and risks to leadership and cross-functional stakeholders.

Requirements

Required:

• Extensive experience managing and scaling engineering teams in high-growth environments, with a focus on data infrastructure or distributed systems.
• Deep technical expertise in data modeling, database internals, and large-scale data warehouse/lakehouse architectures.
• Proven track record of architecting cloud-native, scalable data platforms that handle multi-cloud deployments and high-throughput data streams.
• Strong foundation in data governance principles, including metadata management, data lineage, and automated quality enforcement at scale.
• Ability to thrive in high-ambiguity environments, translating broad business goals into specific technical roadmaps and actionable engineering tasks.
• Pragmatic approach to engineering: know when to build for the future and when to deliver immediate value through iterative improvements.
• Excellent communication skills, with the ability to explain complex architectural trade-offs to both technical and non-technical partners.
• Comfort with end-to-end ownership and a desire to build a "full-stack" data foundation that serves as the company's single source of truth.

Preferred:

• 8+ years of experience managing technical teams.
• Experience growing an engineering team and charter through a period of rapid company scaling.
• Experience conducting privacy reviews, threat modeling, and risk assessments for production systems.
• Proven track record of designing and implementing privacy infrastructure serving millions of users.
• Experience at companies during periods of hypergrowth where you've scaled privacy alongside the business.
• Exposure to AI/ML infrastructure and the unique privacy demands of large-scale training and inference.

Compensation

• Annual Salary: $405,000 - $485,000 USD

Logistics

• Locations: San Francisco, CA and Seattle, WA (United States)
• 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
• Location-based hybrid policy: currently expected to be in one of Anthropic's offices at least 25% of the time
• Visa sponsorship: Anthropic states they do sponsor visas and retain an immigration lawyer to assist where possible

Benefits

• Competitive compensation and benefits
• Optional equity donation matching
• Generous vacation and parental leave
• Flexible working hours
• Office space for collaboration

How we're different

Anthropic focuses on large-scale, high-impact AI research and values communication and collaboration across research and engineering teams. Recent research directions include work related to GPT-3, interpretability, scaling laws, and learning from human preferences.

Application notes

Candidates are encouraged to apply even if they do not meet every qualification. Anthropic provides guidelines for candidate AI usage during the application process and cautions about recruitment scams (recruiters will contact only from @anthropic.com addresses).