# Software Engineer, Robotics Data at WorldEngine AI > Onsite · United States · Unspecified · Software engineering · open. Source: https://alderhq.app/jobs/ashby%3Aworldengine%3Ab9ee30e6-5c47-44e2-8bd3-64ee76989aab (read 2026-11-06). ## Facts (observed) - Company: [WorldEngine AI](https://alderhq.app/companies/worldengine-ai) - Location text: SF Bay Area - Where it can be done from (inferred): Onsite · United States (evidence: "Location: SF Bay Area") - Posted: 2026-10-28 (9 days ago) - First seen by alder: 2026-11-02 · last verified: 2026-11-04 - Reposted: 0 times · edited: 0 times - Apply on the employer's site: https://jobs.ashbyhq.com/worldengine/b9ee30e6-5c47-44e2-8bd3-64ee76989aab - Skills listed (inferred): Python, Go, Rust, AWS, GCP, Docker ## Pay and sponsorship - Posted pay: not posted - Market band for similar posted roles (SF Bay Area, n 1630): p25 $161k, median $200k, p75 $242k - H-1B filings at this company for similar roles, FY26: 1 ([US DOL OFLC LCA disclosure data](https://www.dol.gov/agencies/eta/foreign-labor/performance)) ## Description (the employer's text) ### About the Role Frontier AI is going physical, and the labs building it are bottlenecked on one thing: high-quality data from the real world. We pair operational scale with the specialized engineering that physical-world data demands. As a Software Engineer on Robotics, you'll sit between our 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, working directly with the engineers and researchers at the labs and robotics companies building physical AI, this role is for you. ### What You'll Do Own the reusable infrastructure behind robotics data deliveries: the processing, packaging, and delivery systems Format and transform datasets to per-client specification: MCAP and other container formats, custom schemas, field mappings, metadata, and versioning Work directly with customer engineering teams to scope and build bespoke schemas, custom fields, and one-off transforms where requirements are custom Build and operate the pipelines that move data from our systems into customer storage reliably at petabyte scale Partner with operations and product to turn evolving requirements into shipped data deliveries, and prototype quickly when a new data type or customer arrives Build automated validation techniques ### What We're Looking For 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 ### Nice to Have 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 --- Data: alder (https://alderhq.app). Cite https://alderhq.app/jobs/ashby%3Aworldengine%3Ab9ee30e6-5c47-44e2-8bd3-64ee76989aab and the date you read it. Observed facts come from the employer's own listing; location reach, level, family and skills are inferences.