Software Engineer, Robotics

Mercor's mission is to organize human intelligence to power the AI economy. We're a leading AI data company, building the layer between human expertise and frontier models. Millions of domain experts on the platform are paid over $4 million per day to train frontier AI models. Mercor's APEX benchmark family measures AI's real-world impact on professional work. Mercor Enterprise brings this same infrastructure to Fortune 500 companies: helping companies capture how their best people actually work, translating that expertise directly back into agents.

Mercor is creating a new category of work where expertise powers AI advancement. Achieving this requires an ambitious, fast-paced and deeply committed team. You’ll work alongside researchers, operators, and AI companies at the forefront of shaping the systems that are redefining society. Mercor is a profitable Series C company valued at $10 billion. We work in-person five days a week in our San Francisco, NYC, or London offices.

Frontier AI is going physical, and the labs building it are bottlenecked on one thing: high-quality data from the real world. Mercor pairs its operational scale with the specialized engineering that physical-world data demands.

As a Software Engineer on Robotics, you'll sit between Mercor's data systems and our customers' infrastructure. Every frontier lab wants something different: container format, schema, fields, and quality requirements. You'll build infrastructure general enough that a new customer becomes a quick configuration, and you'll work directly with customer engineering teams on bespoke requests, feeding what you learn into scalable platform architecture decisions. An example might be modifying the segmentation model derived from a customer request for stricter filtering on hands being in frame and building this into a configurable pipeline feature.

Prior robotics experience is not required. This is a backend and data engineering role at its core, developing pipelines, formats, and storage methods. If you've built high-volume data infrastructure anywhere and want depth in robotics data and directly, working directly with the engineers and researchers at the labs and robotics companies building physical AI, this role is for you.

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  Own the reusable infrastructure behind robotics data deliveries: the processing, packaging, and delivery systems

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  Format and transform datasets to per-client specification: MCAP and other container formats, custom schemas, field mappings, metadata, and versioning

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  Work directly with customer engineering teams to scope and build bespoke schemas, custom fields, and one-off transforms where requirements are custom

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  Build and operate the pipelines that move data from Mercor's systems into customer storage reliably at petabyte scale

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  Partner with operations and product to turn evolving requirements into shipped data deliveries, and prototype quickly when a new data type or customer arrives

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  Build automated validation techniques

• Strong backend and data engineering fundamentals in a modern language (Python, Go, Rust) and comfort operating production systems on AWS and GCP

• Experience building and owning high-volume data pipelines, not just contributing to them

• Experience with large binary formats, streaming ingestion, distributed batch processing, and object storage economics

• Customer-facing instincts: you can lead a technical conversation, ask the right questions, and push back when a request is unreasonable

• Comfort working through ambiguity and shipping iteratively with a product team, where requirements evolve with the customers and data types

• Experience with robotics/AV data formats and tooling such as MCAP, ROS, protobuf, Foxglove

• Prior work on data engines, pipelines, or delivery for multimodal data

• Multi-sensor data experience across video, depth, inertial, and audio, including calibration and synchronization

• Forward-deployed, solutions, or delivery engineering experience at a data or infrastructure company

• Computer vision or multimodal ML exposure: detection, tracking, or VLM-based labeling and QC