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Product Manager, AI Research & Engineering

Job in San Francisco, San Francisco County, California, 94199, USA
Listing for: Collectors Universe, Inc.
Full Time position
Listed on 2026-10-03
Job specializations:
  • Business
    AI Business & Operations, AI Evaluation, Business Analyst
Salary/Wage Range or Industry Benchmark: 166000 - 269000 USD Yearly USD 166000.00 269000.00 YEAR
Job Description & How to Apply Below
Position: Staff Product Manager, AI Research & Engineering

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.

As part of our interview process, we request that candidates have their cameras on during video interviews. This helps foster meaningful conversation and allows us to create an experience that closely resembles our standard working environment. Certain interview steps may take place by phone. For remote roles, and at our discretion, candidates may be asked to participate in an on-site interview as part of the final stages of the process.

We understand there may be occasional circumstances requiring accommodation and are happy to discuss them as needed.

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.

What

You’ll Do
  • 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 specialties and locations.
  • Own human-in-the-loop workflows:
    Partner with subject matter experts and power users to define review processes and feedback loops that turn human corrections into valuable training data.
  • Partner across functions:
    Work closely with ML Engineering, Operations, and subject matter experts to set direction, align priorities, and drive execution without relying on direct authority.
  • Measure and iterate:
    Define the metrics that demonstrate data quality, model improvement, throughput, and cost efficiency, and communicate progress and tradeoffs to senior leadership.
  • Raise the bar across Product:
    Mentor other Product Managers, contribute to product practices, and build documentation and mechanisms that help scale product thinking across the organization.
  • Build with AI:
    Use emerging AI…
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