Data Operations Manager, Computer Use & Tool Use

About Anthropic

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.

About the Role:

As Data Operations Manager for Computer Use & Tool Use, you'll build and scale data operations that advance Claude's computer use capabilities and tool use safety. You'll partner with research teams to design and execute data strategies, manage vendor relationships, and own the entire data pipeline from requirements to production. This is a zero-to-one role requiring technical depth to understand what makes high-quality training data for autonomous agents, but your focus will be on strategy and execution rather than hands-on engineering. Think technical founder who evolved from writing code to building the business.

About the Impact:

The data strategies and operations you build will directly determine how well Claude can use tools safely, operate computers autonomously, and maintain quality across long-horizon agentic workflows. You'll work with world-class researchers advancing frontier capabilities, safety, and model performance while building the operational infrastructure to scale these efforts.

We're looking for someone who gets excited about the challenge of scaling quality for complex, multi-turn agent interactions - someone who can think strategically about data needs for both capabilities and safety, build the right partnerships, and execute flawlessly. If you thrive at the intersection of technical depth and operational excellence, we'd love to hear from you.

Responsibilities:

• Develop and execute data strategies for computer use, tool use safety, and agentic AI research

• Partner with research leaders to translate technical requirements into operational frameworks

• Build data collection and evaluation systems for complex scenarios: prompt injection robustness, multi-turn agent conversations, adversarial attacks, autonomous workflows

• Scale the generation of realistic evaluation environments that capture real-world tool use and computer use challenges

• Identify, evaluate, and manage specialized contractors and vendors for technical data collection

• Implement quality control processes to ensure data meets training requirements for both capabilities and safety

• Manage multiple complex projects simultaneously, balancing research velocity with rigorous evaluation standards

• Track metrics and communicate progress to stakeholders

You may be a good fit if you:

• Have 3+ years in technical operations, product management, or entrepreneurial experience building from zero to scale

• Have strong technical foundations - proficiency in Python and understanding of ML workflows, RL environments, and evaluation frameworks

• Have strong communication skills and can effectively engage with both technical and non-technical stakeholders

• Are familiar with how LLMs work and could describe concepts like RLHF, tool use, and agentic workflows

• Understand the unique challenges of evaluating autonomous systems and long-horizon agent behaviors

• Are highly organized and can manage multiple parallel workstreams effectively

• Have a high threshold for navigating ambiguity and can balance strategic priorities with rapid execution

• 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 safe, capable agentic systems

Strong candidates may also have:

• Experience at companies training AI models, building AI agents, or creating AI training data, evaluations, or environments

• Knowledge of computer and tool use safety challenges like prompt injection, data exfiltration attempts, or adversarial attacks

• Experience with RLHF, reinforcement learning techniques, or similar human-in-the-loop training methods

• Domain expertise in computer use automation, security, or AI safety evaluation

• Familiarity with model performance monitoring, training observability, or quality assessment systems

• Track record of building and scaling operations teams

The expected base compensation for this position is below. Our total compensation package for full-time employees includes equity, benefits, and may include incentive compensation.

Annual Salary:
$250,000—$365,000 USD

Logistics

Education requirements: We require at least a Bachelor's degree in a related field or equivalent experience.

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.

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.

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