Staff Product Manager, AI Research & Engineering

About the role

Collectors is the leading creator of innovative technology that provides value-added services for collectors worldwide. We grade, authenticate, vault, and sell millions of record-setting collectibles, all while modernizing and digitalizing the process to further our mission of helping collectors pursue their passions. We’re always on the lookout for talented people to join our growing team. Our services span collectible trading cards, autographs, comic books, coins, video games, event tickets, and memorabilia. Our subsidiaries include PSA, PCGS, Beckett, SGC, and Card Ladder. Since our founding in 1986, we have graded and authenticated millions of items. We employ more than 3000 people across our headquarters in Santa Ana, California and offices in New Jersey, Texas, Florida, Japan, Shanghai, Hong Kong, Canada, Mexico, Germany, the UK, and France.

Overview This team, AI Research & Engineering (AIRE), builds the applied machine learning systems that run our business at scale, from prototype to production. This role is ideal for a product manager who is equally comfortable sourcing training data and writing evals for our models, standing up production-ready pipelines, and shipping tools that help internal teams make better decisions. You'll work across computer vision, structured data, and agentic AI workflows, with direct impact on products used by graders, researchers, and collectors. Every one of those products is only as good as the data behind it, and you'll own that data from first capture to final correction. As a Staff Product Manager, you'll decide what training data we collect, in what order, and how human corrections flow back into our models, partnering closely with ML Engineering and subject matter experts to improve model accuracy, development speed, and cost across the portfolio. You'll bring the technical fluency to earn credibility with engineers, the judgment to turn ambiguous problems into clear strategy, and the systems thinking to build feedback loops that make every model better than the last.

Responsibilities

• Set strategy and make the calls: Define the strategy for our training data capabilities, prioritize what data we collect and when, and own decisions around quality, review depth, confidence thresholds, and cost.
• Build the data collection pipeline: Own data collection end to end, including procurement, preparation, capture, expert labeling, and annotation, and build scalable systems across each stage.
• Optimize for throughput: Coordinate work across teams and sites to maximize throughput, identify bottlenecks, and drive capacity planning across special