Sobre este puesto de Software Engineer - Calibration en Woven By Toyota
TEAM
WHO ARE WE LOOKING FOR?
The Sensor & Calibration sub-team is seeking a Software Engineer to support the development of ground truth sensor calibration & data validation tools that live in the cloud and fuel our next-generation autonomous driving systems.
The selected candidate will be responsible for accelerating our calibration & data validation systems as well as maintaining & improving its infrastructure used across multiple teams. The scope of this role encompasses the following: (1) Developing and scaling validation pipelines that process large quantities of LiDAR data in the cloud, (2) Collaborating with hardware and data engineers to supporting sensor data validation across a complete sensor set up: cameras, LiDARs, imus & radars, by (3) Authoring SQL-based validation tests to ensure data correctness and coverage, (4) Managing the teams calibration and validation services, ensuring robust and predictable operation, and (5) Improving the maintainability, reliability, and observability of these systems, (6) Maintaining and improving the team repositories, with an emphasis on simplification and long-term code health, (7) Evolving the teams Vision library to be more extensible, enabling other engineering teams to build reliably on top of it. Furthermore, candidates who exhibit a collaborative, "giver" mindset, proactively supporting partner teams while maintaining a strong focus on production-quality, scalable solutions, are highly valued.
RESPONSIBILITIES
Support the design, implementation, and delivery of calibration & validation pipelines alongside its underlying infrastructure, proactively resolving deficiencies and ensuring that solutions meet objectives for reliability, maintainability, and business impact.
Write high-quality code & workflows that scale to process large quantities of fleet data, maintaining stable cloud-based calibration & validation services.
Contribute SQL-based validation tests and collaborate with hardware and data teams to ensure comprehensive sensor data validation coverage.
Enable other engineering teams to build reliably on top of shared infrastructure, improve and simplify our in house Vision library.
Instrument and monitor production systems, proactively identifying and addressing issues to uphold service reliability and observability.
Operate within a high-velocity environment and employ rigorous agile development practices
Work within a hybrid workspace model, necessitating physical presence in our London office three days per week.
MINIMUM QUALIFICATIONS
Bachelor's (Master's preferred) in Computer Science, Software Engineering, or a related field, or equivalent industry experience.
Proficiency in Python and SQL, with experience writing production-quality, testable, and modular code.
Experience building and maintaining cloud-based data pipelines, with a strong emphasis on reliability, maintainability, and scalability.
Experience managing and maintaining software repositories, including code reviews, dependency management, versioning, and enforcing contribution standards across multiple teams.
Experience with monitoring and observability tooling to maintain and improve production service health.
The ability to thrive in a fast-paced environment and collaborate effectively across teams and disciplines, supported by excellent interpersonal skills.
NICE TO HAVES
Experience with large-scale data processing frameworks (e.g., Spark, BigQuery, or similar) for cloud-based validation pipelines.
Familiarity with autonomous driving sensor modalities, particularly camera, LiDAR, IMU and/or Radar.
Prior contributions to shared platform or framework libraries used by multiple engineering teams, with an emphasis on developer experience and extensibility.
Experience managing or contributing to backfill and data reconciliation services in a production environment.
Hands-on experience with the full lifecycle of a validation or quality assurance system, from design through deployment and ongoing maintenance.
A proven track record of improving system observability and operational reliability in a cloud-native or distributed systems environment.
