Product Manager, Loop

The Role

Config is building the data and model layer for real-world robotic automation. Loop is our unified platform for turning real-world demonstrations into deployable, task-specific robot policies. Robotics and physical AI come wrapped in buzzwords that make the field feel hard to get into; Loop is built to do the opposite, giving people kind, approachable access to working with robots.

We're looking for a Product Manager to help shape Loop. Loop serves two kinds of users, and the product has to work for both.

The first are the people who live in Loop every day — data collectors, curators, and researchers capturing multi-camera demonstrations, validating episodes, converting human data into robot trajectories, fine-tuning CFG-1, and running policies on real robots. Some are our own team and some work alongside our customers, all on the same production-grade platform. Their throughput and the quality of their data decide whether a customer's robot works, so the everyday details of their workflows matter as much as any headline feature.

The second are the enterprise customers who fund the work. We already work with several, including Fortune Global 500 companies, and Loop is how they get from a target task to a working robot. Their requirements shape the roadmap, and the most valuable work is often anticipating what they'll need before they ask, which takes real market understanding.

Holding both at once is the interesting part of the job: you'll move between the craft a hands-on user needs and the demands of a B2B platform, deciding what to build across the pipeline and why. Whatever the surface, the rule is the same — the product follows the user, and the user comes first. You won't do it alone — you'll work with our product team and closely with Engineering, Research, Design, and our Forward-Deployed and Data Studio teams.

Areas You May Contribute To

• Own the Loop roadmap — deciding what to build, in what order, and why.
• Improve the everyday experience of the collectors, curators, and researchers who use Loop, treating the quality of those repeated interactions as part of the product, not a detail.
• Translate model capabilities (CFG-1, the Conversion and Policy Engines) into workflows that are clear and quick to learn.
• Use quantitative metrics, experimentation, user research, and direct observation of operators to understand behavior and set priorities.
• Work with sales, customer success, and Forward-Deployed Engineers to turn enterprise requirements into a focused backlog.
• Stay close enough to customers and the wider robotics-automation field that their next requirement is rarely a surprise.
• Define the deployment, integration, and fleet-administration capabilities that make Loop adoptable inside large organizations.
• Balance long-term platform investment against the immediate needs of live customer deployments.
• Communicate clearly in writing — specs, decision docs, and release notes that keep a cross-functional team aligned.

What We're Looking For

• 6+ years of product management experience on technical products, or equivalent evidence of strong ownership and impact.
• Strong product judgment and taste, with a track record of shipping technical products that people depend on.
• Comfort with B2B and go-to-market work — engaging enterprise customers directly and translating their needs into product.
• Fluency with data, experimentation, and user research as tools for making decisions.
• The ability to reason about complex, multi-stage systems and make sound tradeoffs across data, models, and tooling.
• Enough technical depth to work closely with engineers and researchers — you don't need to train the model, but you should understand how it behaves.
• Strong ownership and comfort in a fast-moving, ambiguous environment.
• Excellent communication in Korean and written English.
• A genuine interest in robotics, foundation models, and bringing Physical AI into the real world.

Bonus Points

• Experience with products that ask for both consumer-grade craft and B2B deployment.
• A background in engineering, research, or founding a company before moving into product.
• Experience building tools for internal operators, annotation, or data collection.
• Experience with robotics, ML/AI platforms, data infrastructure, or developer tools.
• Familiarity with the robot-learning landscape (imitation learning, teleoperation, VLA/RFM systems).
• Startup experience, or another high-ownership role at an early-stage company.