About the Role
Anthropic is seeking an entrepreneurial Data Operations Manager to spearhead human data collection initiatives that directly power our most advanced AI research capabilities. You'll work across our Coding, Computer Use, and Alignment Science teams to design, build, and scale complex data collection projects that push the boundaries of what's possible with agentic AI systems.
This is a highly strategic, 0-to-1 role designed for someone with a strong software engineering background who has evolved into driving business outcomes - think CTO of a startup or technical founder who thrives at the intersection of technology and operations. You'll be responsible for creating novel data collection methodologies, building scalable processes from scratch, and solving complex technical challenges that directly improve Claude's coding and computer use capabilities.
You will own "data as the product" for our most critical AI research initiatives, translating cutting-edge research needs into robust, scalable data collection systems. This role requires the technical depth to understand complex AI systems combined with the strategic mindset to build and scale operations that support frontier model development.
About the Impact
As our Data Operations Manager, you'll be at the forefront of developing the data infrastructure that enables breakthrough advances in AI capabilities. Your work will directly contribute to making AI systems more capable at complex reasoning, coding, and computer use tasks while maintaining our commitment to safety and alignment. This role offers the unique opportunity to shape how frontier AI systems are trained and evaluated, working with some of the most advanced AI research in the world.
If you're excited about building something entirely new, have the technical depth to understand complex AI systems, and possess the entrepreneurial drive to create scalable operations from the ground up, we'd love to hear from you.
Responsibilities
Strategic Leadership & Vision
- Lead the development and execution of comprehensive data strategies for agentic AI research, including environments for advanced coding capabilities, computer use, and safety evaluations
- Drive strategic initiatives that directly impact model performance and capabilities, making decisions that affect data quality, operational efficiency, and research velocity
- Collaborate with research leaders across teams to understand complex technical requirements and translate them into scalable operational frameworks
Technical Infrastructure & Innovation
- Design and build novel data collection systems and evaluation frameworks that enable rigorous measurement of AI system capabilities
- Architect scalable, automated infrastructure for collecting, processing, and managing high-quality human feedback data across multiple research domains
- Develop sophisticated tooling and platforms that support complex human-AI interaction scenarios, particularly for coding and computer use tasks
Cross-functional Partnership
- Partner closely with researchers, engineers, and product teams to ensure data collection systems integrate seamlessly with training pipelines and research infrastructure
- Work directly with technical stakeholders to scope complex projects, resolve technical blockers, and ensure successful implementation of data collection strategies
- Serve as a technical bridge between research teams and operational execution, ensuring alignment on both technical requirements and business outcomes
Operational Excellence & Scaling
- Build and manage relationships with specialized contractors and vendors who can execute on highly technical data collection requirements
- Implement robust quality control and verification processes to ensure data usability for training state-of-the-art AI systems
- Drive continuous improvement in efficiency, quality, and cost-effectiveness while maintaining the highest standards for frontier AI research
Project Execution & Management
- Manage multiple complex, high-stakes projects simultaneously, balancing technical complexity with delivery timelines
- Create analytics and measurement frameworks to make data-driven decisions about project prioritization and resource allocation
- Establish processes that enable rapid iteration and experimentation while maintaining rigorous quality standards
You may be a good fit if you
- Have 5+ years of software engineering experience with a proven track record of building complex technical systems
- Demonstrate entrepreneurial experience as a technical founder, CTO, or in similar 0-to-1 leadership roles where you've built both technology and business processes
- Are proficient in Python, data systems architecture, and have deep understanding of machine learning workflows and evaluation frameworks
- Have experience with agentic AI systems, code generation models, large language models, or AI safety research methodologies
- Possess exceptional project management skills with ability to coordinate complex technical initiatives across multiple teams
- Are comfortable operating in highly ambiguous environments where you need to define both the "what" and the "how" from scratch
- Have a strong technical intuition for what makes high-quality training data for advanced AI systems
- Thrive in fast-paced research environments with shifting priorities and novel technical challenges
- Are passionate about AI safety and understand the critical importance of high-quality data in building beneficial AI systems
Strong candidates may also have
- Experience building or working with AI agents, computer use capabilities, or advanced coding assistance tools
- Background in designing and implementing evaluation systems or human-in-the-loop workflows for large language models
- Experience with reinforcement learning, constitutional AI, or other advanced AI training methodologies
- Knowledge of sandboxed execution environments, security considerations for AI systems, or automated code evaluation
- Experience in high-growth startup environments, particularly in technical roles that evolved to include business responsibilities
- Background collaborating with AI researchers or experience in research-oriented organizations
- Experience with prompt engineering, red teaming, AI safety evaluation, or related AI safety methodologies
- Technical expertise in areas like distributed systems, data pipelines, or ML infrastructure
Role Specific Location Policy:
- This role is based in San Francisco office; however, we are open to considering exceptional candidates for remote work on a case-by-case basis.
