Software Engineer - Perception at Watney
San Francisco, CA
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Jul 12, 2026
San Francisco, CA
Jul 21, 2026
What this job asks for AI summary
A backend or data infrastructure engineering role focused on building and scaling the data platform that underpins autonomous robot perception. Day-to-day work spans designing petabyte-scale pipelines for video and sensor ingestion, building regression and evaluation infrastructure for edge-deployed perception models, and creating automated annotation and feature extraction workflows. Suits engineers with backgrounds in large-scale distributed systems or autonomous robotics data infrastructure.
Senior level · National · Full-time
Posted 2 times — it's one opening, so apply once.
We read this from the posting text with AI. Skim the description below before ruling yourself out.
Why we read it this way (6)
No work location is specified in the posting — CBSA and state are left blank. The company (Watney Robotics) appears to be a US-based startup, so the US-based flag defaults to true.
No compensation, experience-year floor, or degree requirement is stated anywhere in the posting.
The role sits at the boundary between data/pipeline architecture (15-1243) and backend software engineering (15-1252). The primary framing — 'data engine', 'streaming architectures', 'evaluation platforms', 'pipelines' — leans toward data infrastructure architecture, but the volume of software-building language ('architect and optimize', 'design and implement', 'develop high-throughput pipelines') makes 15-1252 a credible runner-up.
All skills are drawn from the 'What You'll Do' and 'You May Be a Good Fit If You' sections. The posting explicitly encourages applicants who don't meet every requirement, and the 'Good Fit' framing is softer than a hard-gate 'Requirements' section — however, the four 'Good Fit' bullets describe the core competencies the role is built around and are treated as required. Computer vision, 3D spatial data, and ML orchestration appear as a single 'exhibit' bullet alongside the core pipeline skills and read as more contextual/preferred depth rather than primary gates.
No specific named technologies (e.g. Kafka, Spark, Kubernetes) are called out anywhere in the posting — all skills are described at the capability/concept level.
Ignored 2 non-technology phrase(s) as skills (responsibilities/concepts, not named tools): data pipeline architecture, ML workflow orchestration.
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