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WorldEngine AI

Software Engineer, Robotics Data

  • Seen live on WorldEngine AI's site yesterday
  • Mid level
Pay
$130k to $400k a year USD
Location
SF Bay Area
Open for
6+ days
First seen
Oct 29, 2026
Apply on WorldEngine AI's site (opens in a new tab)

Posting record

No flags

Open 6+ days

The posting's record, oldest first
  1. WorldEngine AI's site dates the posting
  2. Ghostless first sees this listing
  3. Last checked: still up
How we know

It hasn't been reposted, listed twice, or left up unusually long, and the posting doesn't say it's a talent pool.

Ghostless records the day it first sees a posting, so a repost can't reset its age. This one was already up when Ghostless started tracking WorldEngine AI, so its true age is longer.

Last checked Nov 3, 2026.

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What the posting discloses

Pay
$130k to $400k a year USD

Not stated: Real vacancy, AI screening.

Why these three

Ontario requires postings from employers with 25 or more staff to state the pay, whether the vacancy exists, and whether AI screens applicants.

Skills mentioned

About the role

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

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