Overview
Woven by Toyota is enabling Toyota’s once-in-a-century transformation into a mobility company. Inspired by a legacy of innovating for the benefit of others, our mission is to chall
Full job description
Woven by Toyota is enabling Toyota’s once-in-a-century transformation into a mobility company. Inspired by a legacy of innovating for the benefit of others, our mission is to challenge the current state of mobility through human-centric innovation — expanding what “mobility” means and how it serves society.
Our work centers on four pillars: AD/ADAS, our autonomous driving and advanced driver assist technologies; Arene, our software development platform for software-defined vehicles; Woven City, a test course for mobility; and Cloud & AI, the digital infrastructure powering our collaborative foundation. Business-critical functions empower these teams to execute, and together, we’re working toward one bold goal: a world with zero accidents and enhanced well-being for all.
TEAM Woven by Toyota’s Release & Triage team operationalizes continuous improvement for automated driving by turning on-road signals and incidents into actionable engineering insights. The Release & Triage Tooling sub-team builds AI- and data-driven internal tools and services that support release qualification, large-scale simulation-based testing, and high-quality failure analysis, partnering closely with Release, Triage, and core development teams to keep the mainline stable and continuously improving.
WHO ARE WE LOOKING FOR? We’re looking for a mid- to senior-level Software Engineer to design and build internal tooling that uses AI/ML and LLM-based approaches to interpret complex signals from logs, metrics, test results, images, and video, helping QA and development engineers quickly understand failures, identify regressions, and make confident release decisions. You will be part of a team that owns end-to-end services and workflows that power release qualification and triage at scale, integrating with CI/CD, simulation platforms, and internal data pipelines while helping establish best practices for responsible and effective use of AI-driven analysis within the team.
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