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Salary
≈ $94k – $184k per year (Estimated)
Location
Hybrid (Ann Arbor, United States)
Seniority
Middle · 3+ years exp
Visa
Sponsors visas in United Kingdom; no register of this country names it

Confirmed on the employer's own hiring board on Nov 11, 2026. First seen by Alion on Sep 16, 2026. Woven by Toyota scores B on the Alion truth index.

Overview
Company
Impact
Profile match
Woven by Toyota is the software and advanced development arm of Toyota, established in 2018 and restructured into its present form in 2023, and it builds the vehicle software the group intends to run across its future models. Its work covers the Arene vehicle software platform, automated driving and safety systems, and Woven City, a purpose-built test community at the foot of Mount Fuji where the group can trial mobility technology in a real inhabited environment. Headquartered in Tokyo, it represents Toyota's attempt to develop software competence internally rather than depend on suppliers.

TEAM

The Data & Labeling Services (DLS) team within AD/ADAS at Woven by Toyota generates, validates, and delivers structured, high-quality offline perception and map data. This data supports autonomy development, evaluation, and training through efficient auto-labeling, rigorous quality assurance, and actionable data science insights. The candidate will join the Label Production team and collaborate closely with perception and ML teams across Woven.

WHO ARE WE LOOKING FOR?

We are looking for a curious and motivated software engineer who is excited to help build and operate scalable production pipelines that turn autonomous vehicle sensor data into high-value labels with high-definition map features for cutting-edge ML models. If you are naturally inquisitive, eager to learn, independent yet deeply collaborative, and passionate about technology’s potential, you will thrive here. You will work at the intersection of data engineering, workflow orchestration, and geospatial algorithms, owning components end-to-end while learning alongside experienced perception, ML, and data teams. We value a strong growth mindset and team-first attitude over a set number of years on a resume.

RESPONSIBILITIES

  • Data and pipeline engineering

  • Contribute to building and optimizing end-to-end data pipelines that ingest real vehicle data and localize it against HD maps.

  • Learn to build and maintain workflow orchestration for long-running production jobs, focusing on reliability and efficiency.

  • Collaborate on designing clean data models, schemas, and tooling that enable adjacent teams to access insights easily.

  • Quality, testing, and operations

  • Learn and apply robust testing strategies across data pipelines, including unit, integration, and regression testing.

  • Participate in root-cause analysis for pipeline issues, helping ensure system health and smooth releases.

  • Collaboration & Ownership

  • Work closely with ML/robotics/data engineers, data annotators, product managers, and map vendors to align on workflows and requirements.

  • Take ownership of end-to-end features, from initial technical design to documentation and release.

MINIMUM QUALIFICATIONS

  • Bachelor's degree in Computer Science, Engineering, or a related field (MS/PhD preferred but not required), or equivalent practical experience.

  • 3+ years of professional software development experience building backend services and data pipelines.

  • Strong proficiency in Python and basic knowledge of SQL.

  • Strong problem-solving ability and an eager, growth-oriented mindset.

  • Proficiency with Git for version control in a collaborative, review-based workflow.

  • Experience with cloud infrastructure, with a preference for AWS, plus Docker and CI/CD systems such as GitHub Actions.

  • Demonstrated passion for backend software development and data engineering.

  • Great team player with solid written and verbal communication skills.

NICE TO HAVES

  • Background or strong interest in autonomous driving, robotics, or geospatial mapping technologies.

  • Hands-on experience with geospatial data formats (e.g., GeoPackage), spatial indexing (e.g., KD-trees), desktop GIS tools (e.g., QGIS), and relational databases (e.g., PostgreSQL).

  • Understanding of 3D geometric coordinate transformations, spatial data processing, and 3D visualization.

  • Familiarity with multimodal 3D sensor modalities and calibration techniques (e.g., Cameras, LiDAR, IMU).

  • Enthusiasm for leveraging AI-assisted development tools (e.g., Copilot, Cursor, Claude Code) to accelerate developer velocity.

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