Product Management, Human Data Platform

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.

Anthropic's Human Data Platform team builds systems designed to collect data that improves our models. This includes the infrastructure to simulate real-world environments and tasks, novel interfaces for data vendors to use, and the pipelines that enable researchers to gather high-quality data at scale. As Claude's real-world usage evolves, so do our data needs — and our tooling has to keep pace. You'll work alongside an engineering team that's quickly prototyping and shipping, help make smart bets about where to focus, and ensure we're investing in tooling that scales. You'll work across research teams, data ops, and external vendors, translating what you learn into clear direction on what to build next.

You May Be a Good Fit If You

Strong Candidates May Also Have

• Own the product direction for our human data tooling, with clear prioritization across labeling interfaces, infrastructure investments, data quality, and operational visibility
• Partner with engineering to scope and ship quickly, staying close to the work in a fast-moving prototyping environment
• Develop a deep understanding of research and training approaches to identify where tooling investments will have the highest leverage
• Identify patterns across one-off requests and push toward reusable infrastructure that compounds over time
• Sit in on crowd worker and vendor sessions to systematically understand pain points
• Define and track outcome-based KPIs: time-to-launch for new data collection projects, end-to-end data quality scores, and measurable impact on model evaluation scores
• Believe that advanced AI systems could have a transformative effect on the world and are interested in helping make sure that transformation goes well
• Are drawn to ambiguous, high-stakes environments where you’ll play a big role in defining the product strategy
• Shipped products where they had to deeply understand technical constraints, not just translate requirements
• Experience working directly with research teams, ideally in AI/ML contexts
• Are equally comfortable talking to crowdworkers about their workflow and to research teams about data quality methodology
• Are a quick study—this team sits at the intersection of a large number of different complex technical systems that you'll need to understand (at a high level) to be effective
• Have an interest in how humans interact with AI systems and how to design experiences that elicit high-quality data
• Experience building data collection tools, annotation platforms, or human-in-the-loop pipelines
• Experience working with researchers who are internal users/customers
• Good instincts and an eye for intuitive user experiences, particularly those involving complex UI interactions or annotation workflows
• Strong project management skills: prioritization, communicating across team/org boundaries

The annual compensation range for this role is listed below.

For sales roles, the range provided is the role’s On Target Earnings ("OTE") range, meaning that the range includes both the sales commissions/sales bonuses target and annual base salary for the role.

• Intellectual curiosity without ego: Comfortable not knowing things and asking questions to learn
• Autonomous learning: Able to independently figure out how systems work and develop expertise quickly
• Researcher EQ: Deep understanding of how researchers think and what motivates them
• Product creativity: Ability to see novel product opportunities emerging from research capabilities

$305,000—$385,000 USD

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.

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.

Your safety matters to us. To protect yourself from potential scams, remember that Anthropic recruiters only contact you from @anthropic.com email addresses. In some cases, we may partner with vetted recruiting agencies who will identify themselves as working on behalf of Anthropic. Be cautious of emails from other domains. Legitimate Anthropic recruiters will never ask for money, fees, or banking information before your first day. If you're ever unsure about a communication, don't click any links—visit anthropic.com/careers directly for confirmed position openings.

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.