Trace is building the data marketplace for physical AI.
Physical AI has the potential to transform how work gets done in the real world, from robotics to embodied systems that can see, move, and interact with their environment. But today, progress is constrained by a fundamental limitation: there is no scalable way to collect high-quality, real-world training data. Frontier robotics models are trained on orders of magnitude less data than language models because there is no equivalent of an “internet of robotics data.”
Trace exists to change that.
We build the infrastructure that makes it possible to capture and transform real-world data from humans performing physical work, and turn it into training data for robotics systems, embodied AI models, and other AI systems that operate outside the browser and in the physical world.
If we succeed, we meaningfully accelerate the development of physical AI and expand what these systems can safely and reliably do in the world. Our platform is designed to support many data formats, capture workflows, and customer needs over time. What we capture today is only the starting point.
If you want to be an early hire at a company helping define how robots learn to work, keep reading.
A world-changing problem: Physical AI will reshape entire industries, but it cannot scale without real-world data. Trace is addressing one of the core constraints holding the field back.
Early but real traction: Active pilots with growing demand on both sides of the marketplace.
Experienced, tight-knit team: Ex founders, PhDs, and operators with a track record of building and scaling together.
Real ownership: This is early. Your work will materially shape the product, systems, and direction of the company.
Foundational platform: We are building core infrastructure that enables many future products and use cases as physical AI evolves.
We are hiring generalist software engineers who are excited to build across the stack and own meaningful systems end to end. The work spans backend services, data pipelines, internal tools, and product surfaces that support real-world data capture and delivery.
Our core services are written in Python. We use TypeScript (React), PostgreSQL, and C# (Unity). We're hosted on AWS via Terraform. Having these skills is great, but we know the best engineers can pick them up quickly when needed. We also expect our tech stack to evolve over time. What matters most is strong engineering fundamentals, comfort with ambiguity, and a desire to build things that actually ship and get used.
Build and evolve core systems that move data from capture to customer-ready datasets
Own projects end-to-end and work directly with customers on both sides of the marketplace to inform product decisions.
Work across backend services, data pipelines, internal tooling, and product workflows
Improve reliability, performance, and observability of systems operating in messy environments
Help define technical direction and engineering best practices as the team scales
Comfort operating in early-stage environments where you help define structure and process
Strong generalist engineering skills with experience across multiple parts of the stack
Product-minded builder who is comfortable working directly with customers and translating real world feedback into better systems and experiences
Solid backend fundamentals and the ability to reason about systems, performance, and tradeoffs
High ownership, good judgment, and productive, thoughtful communication
Emotional maturity and a collaborative, grounded working style
Exposure to robotics, autonomy, CV, or hardware-adjacent products
Experience with video, media pipelines, or large data systems