The engineer will support the implementation and continuous improvement of engineering quality processes across systems and software development. They are responsible for assessing process adherence, maintaining end-to-end traceability, and ensuring objective evidence is available for product milestones.
The Application Integration Engineer designs, implements, and supports enterprise-wide application ecosystems to ensure optimal performance and secure data flow. They also manage automation tooling, define security standards for APIs, and collaborate with cross-functional teams to optimize system integrations.
The data annotator will label 2D images and 3D LiDAR sensor data to train artificial intelligence systems for autonomous trucks. Responsibilities include reviewing pre-labeled data for quality assurance and maintaining high accuracy standards according to detailed guidelines.
The Data Annotator will label 2D images and 3D sensor data to train AI systems for autonomous trucks. They are responsible for reviewing pre-labeled data and ensuring high-quality annotations through internal software tools.
You will define and implement the vehicle-level test strategy for L4 autonomous trucks, ensuring requirements are met through rigorous verification and validation. You will also lead and mentor a team of test engineers while coordinating with cross-functional departments to manage test budgets and schedules.
You will lead a team of HiL test engineers to define and execute verification and validation strategies for autonomous truck technology. This role involves ensuring compliance with functional safety standards like ISO 26262 and SOTIF while managing test campaigns and cross-functional collaboration.
You will lead a team of simulation engineers to define and execute the virtual verification and validation strategy for autonomous trucks. This involves ensuring compliance with safety standards and collaborating with cross-functional teams to support the safety case.
Lead and ensure technical excellence for Metrics for Release and Hardware-in-the-Loop groups while architecting scalable validation systems. Drive integration of self-driving components, maintain requirement traceability, and mentor engineers on systems design and best practices.
You will design, develop, and deploy production-ready machine learning models for multi-object tracking and sensor fusion in autonomous trucks. Additionally, you will collaborate with cross-functional teams to define evaluation strategies and ensure the safety and robustness of tracking systems.
The Quality Control Engineer will audit high-definition map geospatial datasets to ensure accuracy and completeness for autonomous vehicle operations. They will collaborate with engineering teams to define QC workflows, identify map defects, and drive process improvements.
You will design, develop, and deploy deep learning models for camera-based perception to power autonomous trucks. This involves owning the end-to-end model development lifecycle, from data curation and training to evaluation and production deployment.
You will design, develop, and deploy deep learning models for camera-based perception to power autonomous trucks. This involves owning the end-to-end model development lifecycle, from data curation and training to integration within the autonomy stack.
You will own system-level simulation testing for autonomous driving software and focus on virtual verification and validation. Additionally, you will maintain the scenario library and coordinate with internal and external teams to evolve simulation toolchains.
You will design and maintain scalable AWS-native data pipelines to support ML training, simulation, and analytics for autonomous vehicle fleets. Additionally, you will collaborate with cross-functional teams to ensure data integrity and support the expansion of the enterprise data lake.
You will build and maintain scalable, cloud-based data pipelines to support machine learning training and analytics for autonomous vehicle fleets. Additionally, you will collaborate with cross-functional teams to ensure data integrity and participate in an on-call rotation for system support.
The role involves designing, developing, and improving machine learning models for 3D perception systems within an autonomous driving stack. You will own the end-to-end model development process, from data preparation and training to integration and performance optimization.
The role involves building and maintaining production-grade data pipelines to process petabytes of autonomous vehicle log data. You will collaborate with ML engineers to deploy tagging models and manage AWS infrastructure to support large-scale data processing.
The role involves acting as an embedded point of contact between the simulation platform team and autonomy teams to drive the adoption of simulation tools. You will implement end-to-end data flows, onboard autonomy models for large-scale replay and evaluation, and debug issues across the full stack.
You will design, build, and maintain scalable data infrastructure and pipelines to process high-bandwidth sensor logs from autonomous vehicles. Additionally, you will develop tools for dataset curation, labeling, and visualization to support perception, planning, and simulation engineering teams.
You will lead the Scene Generation team in developing neural rendering and generative models for autonomous vehicle simulation. This involves setting the technical roadmap, mentoring engineers, and ensuring the delivery of production-quality software for sensor simulation.
Design, develop, and deploy production machine learning models for 3D perception and Bird's Eye View capabilities. Collaborate cross-functionally to integrate these models into the autonomous truck software stack and mentor junior engineers.
Lead the technical development of motion planning, prediction, and decision-making systems for autonomous trucks. Drive architectural strategy and engineering excellence while collaborating across autonomy, controls, and safety teams to deploy scalable planning capabilities.
Design and implement cloud-based pipelines to convert multi-sensor data into VLM/VLA training datasets. Develop VLM-assisted auto-labeling systems and curate long-tail scenarios to improve autonomous driving model performance.
Architect and optimize distributed data pipelines to extract safety-critical driving events from massive multi-sensor logs. Develop ML-assisted algorithms for scenario classification and manage the integration of tagged events into an observations database.
