Develop and deploy machine learning models for autonomous truck behavior systems, including reinforcement learning and imitation learning. Collaborate with cross-functional teams to integrate these models into simulation and real-world autonomy stacks.
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.
Own Torc’s litigation docket, outside counsel relationships, litigation readiness program, and legal leadership during incident response. Advise Product, Engineering, Safety & Regulatory, and Government Affairs teams on product risks, regulatory reporting, safety decisions, and the legal implications of commercial autonomous trucking operations.
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 own and evolve the technical architecture for scalable vehicle modeling and simulation while developing high-fidelity dynamics models for autonomous trucks. You will also define statistical validation metrics and drive robustness testing frameworks to ensure safety and performance across simulation pipelines.
You will architect and maintain backend services and data pipelines for map validation, simulation, and annotation platforms. Additionally, you will provide technical leadership to cross-functional teams to ensure high-performance data processing and scalable tooling for autonomous trucking.
Lead the architecture, design, and evolution of core frameworks for the autonomous driving stack, including state management and inter-component communication. Collaborate with cross-functional teams to establish scalable architectural patterns and mentor engineers to ensure high standards of reliability and performance.
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 Senior Analytics Engineer will design and own governed data models and ETL/ELT pipelines to serve as the single source of truth for business intelligence. They will also mentor team members and partner with stakeholders to translate business needs into scalable, reliable reporting solutions.
Provide on-call MLOps support for model development teams by triaging and resolving pipeline issues. Collaborate with senior engineers to improve tooling, documentation, and operational processes across the ML stack.
The Test Safety Engineer will develop and maintain operational safety concepts, processes, and risk assessments for autonomous heavy trucks. They will collaborate with cross-functional teams to monitor safety metrics, investigate incidents, and ensure compliance with safety standards during testing operations.
You will be responsible for building and maintaining robust CI/CD pipelines and managing production infrastructure to support autonomous vehicle software development. Additionally, you will troubleshoot production incidents, participate in on-call rotations, and create comprehensive documentation for engineering tools.
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 Senior Product Cybersecurity Architect will define and implement security controls across the autonomous vehicle platform throughout the product lifecycle. They will collaborate with cross-functional teams to integrate security into design, conduct threat modeling, and ensure compliance with safety and security standards.
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.
The Senior SOTIF Engineer will lead safety assessments and risk analyses to ensure autonomous trucking systems meet rigorous safety and reliability standards. They will collaborate with cross-functional teams, including AI-ML and software developers, to define acceptance criteria and implement design improvements.
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.
Manage and optimize cloud costs across multi-cloud environments using FinOps best practices and Lean-Agile methodologies. Design cost models, implement tagging strategies, and build dashboards for financial and technical stakeholders.
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.
Develop machine learning workflows and AI development tooling to solve complex business problems for automated trucks. Lead projects and collaborate with stakeholders to implement state-of-the-art technology into production.
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.
Lead complex data science projects to improve manufacturing processes and develop machine learning workflows from analysis to embedded deployment. Define data ingestion and governance standards while providing technical leadership and mentoring to team members.
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.
