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Palo Alto, California, United States
Hybrid
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.
Perform root cause analysis of events coming from on-road testing and simulation testing of an automated driving system currently under development.
Partner with release engineers, triagers, and infrastructure teams to understand real-world failure modes, identify high-value automation opportunities, and iterate on models, heuristics, and tooling based on feedback and outcomes.
Build and maintain tooling that uses AI/ML and LLMs to analyze test and release failures across logs, metrics, images, and video.
Design systems that surface meaningful insights, root‑cause hypotheses, and confidence signals to engineers to support release decisions.
Develop tooling that supports release qualification, gating, and readiness assessment, including large-scale simulation-based testing workflows.
Integrate AI-based analysis into CI/CD systems, test frameworks, simulation platforms, and internal data pipelines to create seamless end-to-end workflows.
Design and implement scalable, reliable internal services used by release and triage teams, ensuring maintainability, observability, and performance at scale.
Build dashboards and reports that communicate release health, risk, and trends to stakeholders across Release, Triage, and development teams.
Use AI-assisted coding tools (e.g., GitHub Copilot, Cursor, Claude Code or similar) to accelerate development, refactoring, and testing, while rigorously validating AI-generated code for correctness, security, performance, and maintainability.
Help establish best practices for responsible and effective use of AI code generation and AI-driven analysis within the team.
Write clear documentation and usage guidelines so internal users can effectively adopt and extend the tooling you build.
Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience. Experience with
3+ years of professional software development experience (mid-level) or 5+ years (senior-level).
Strong proficiency in at least one general-purpose programming language (e.g., Python, C++, Rust, Java, Go).
Experience building internal tools, platforms, or infrastructure services used by other engineering or operations teams.
Strong debugging and analytical skills, especially in complex, distributed, or data-intensive systems.
Familiarity with CI/CD systems and release-adjacent workflows, including integration with test frameworks and data pipelines.
Experience applying AI/ML or LLMs to real-world software systems, ideally in tooling or infrastructure contexts.
Hands-on experience using AI-assisted code generation tools in a professional environment, including defining review practices.
Experience working with multimodal data (text, logs, structured data, images, video) and building systems that interpret complex signals.
Familiarity with model evaluation, confidence scoring, and/or explainability techniques for AI-driven analysis.
Experience with simulation-based testing, CI/CD, or release engineering workflows in safety-critical or high-reliability domains (e.g., automotive, robotics, aerospace, medical).
Experience with triage tooling, incident review, and data visualization for operational dashboards.