Jobs at Torc Robotics

Lead product strategy for next-generation autonomous driving technology, including product definitions, user journeys, requirements, ODD specifications, roadmaps, metrics, and safety standards. Collaborate with engineering, regulatory, marketing, commercialization, fleet, and customer teams. Prioritize features, assess competitive products, manage technical and execution risks, track milestones, and mentor junior employees.

Leads a team responsible for simulation-based verification and validation of autonomous trucks. Defines V&V strategy, scenario coverage, test campaigns, toolchain requirements, and safety-case evidence aligned with automotive standards. Partners with product, systems, safety, engineering, tools teams, and vendors to establish release criteria, identify testing gaps, analyze results, and report risks and progress to leadership. The role also involves team development, simulation scenario management, and advancing autonomous vehicle validation practices.

Leads vehicle-level verification and validation for L4 autonomous trucks. Defines test strategies, develops scenario-based test coverage, analyzes ground-truth and onboard vehicle data, and manages test platforms and toolchains. Builds and mentors test engineering teams, oversees budgets and schedules, prioritizes safety risks, and communicates results to leadership. The role also supports safety cases, regulatory and customer audits, software tool qualification, and compliance with automotive safety standards.

Own and operate Torc’s global AWS infrastructure, including multi-account organization management, networking, security posture, Kubernetes and container platforms, CI/CD tooling, artifact distribution, and cloud cost management. Build scalable infrastructure with Terraform, support engineering teams developing robotics workloads, contribute to architecture and documentation, and participate in business-hours on-call rotations.

Leads technical excellence and integration for metrics, release, and Hardware-in-the-Loop systems supporting autonomous driving software. Architects scalable validation platforms, distributed compute and telemetry pipelines, requirements traceability, automated testing, KPI reporting, and subsystem integration. Serves as a systems engineering subject matter expert, drives standards adoption, troubleshoots robotic and automotive systems, and mentors engineers across teams.

Lead and grow a machine learning and software engineering team building production LiDAR and radar perception systems for autonomous trucks. Own technical direction, roadmap, model development, evaluation, integration, deployment, and monitoring. Guide multitask perception, sensor fusion, robustness, and graceful degradation across adverse conditions. Collaborate cross-functionally, manage dependencies and milestones, maintain engineering standards, and communicate risks and progress to leadership.

Develop and deploy machine learning models and production software for multi-object tracking in autonomous trucks. Responsibilities include sensor fusion across camera, LiDAR, and radar; scalable PyTorch training workflows; tracking algorithm development; large-scale data analysis; C++ and Python engineering; safety and validation strategy; architecture decisions; and cross-functional collaboration. The role also provides technical leadership through design reviews, code reviews, mentoring, and engineering best practices.

Develop production-ready real-time C++ software connecting autonomous vehicle systems with mission control, remote assistance, routing, and truck management platforms. Design architectures, algorithms, telemetry, testing frameworks, and verification artifacts for safety-critical vehicle guidance and monitoring. Collaborate across software, systems, perception, controls, and safety teams; support simulation, integration, performance improvements, and automotive standards. Mentor peers and contribute to route planning and machine learning integration for autonomous trucks.

Develop and deploy machine learning models for autonomous truck behavior systems using imitation learning, reinforcement learning, and sequence modeling. Build production ML code, training pipelines, data workflows, evaluation tooling, and inference systems. Analyze model performance and failure modes, collaborate with autonomy and simulation teams, and integrate learned behavior models into simulation and validation workflows.

Reviews and audits high-definition map datasets for accuracy, completeness, and safety-critical defects. Performs quality control on geospatial data derived from LiDAR, imagery, and vehicle sensors; documents and triages issues; defines QC metrics, sampling strategies, and repeatable workflows; collaborates with mapping engineers, autonomy teams, and vendors; analyzes defect trends; maintains procedures; and supports launch readiness for new corridors and operational domains.

Lead on-truck integration and bring-up of embedded compute platforms, sensors, networking, vehicle interfaces, and software releases. Own integration readiness, root-cause investigations, validation environments, automation frameworks, system-level test strategies, and technical documentation. Partner across hardware, software, systems, and autonomy teams to improve integration quality and scalability. Mentor engineers and support architecture decisions through lab and field evidence.

Design and implement scalable mapping data-processing pipelines, APIs, and navigation systems for autonomous trucks. Translate ambiguous requirements into technical designs, build high-throughput services, collaborate with consuming engineering teams, incorporate feedback into shipped features, and debug complex software defects. The role requires programming expertise, AWS experience, strong technical communication, independent problem-solving, and familiarity with source control and agile workflows.

Own system-level simulation testing and virtual verification of autonomous driving software. Plan test campaigns, develop scenarios and test procedures, maintain scenario libraries, identify testing gaps, improve simulation toolchains, analyze results, and communicate dashboards and reports to stakeholders. Support development and integration teams through troubleshooting, fault isolation, and system-level analysis while helping improve autonomous vehicle safety and robustness.

Leads architecture and development of scalable autonomous vehicle simulation and high-fidelity truck dynamics models. Defines validation metrics, robustness testing, and verification strategies supporting safety-case claims, large-scale V&V, and reinforcement learning. Collaborates with autonomy, controls, safety, OEM, and vendor teams; performs C++ development in Linux with ROS/ROS2; investigates simulation and hardware-in-the-loop issues; drives technical direction, testing rigor, and engineer mentorship.

Lead the architecture, design, implementation, and evolution of core C++ frameworks supporting Torc’s autonomous driving stack. Drive technical direction for real-time systems, communication, state management, testing, observability, and validation. Collaborate with Behavior and Perception teams, lead architecture and code reviews, mentor engineers, resolve platform-level issues, and establish scalable software engineering standards for reliable autonomous vehicle operations.

Leads development of BEV and multi-modal perception models for autonomous trucks, combining camera, LiDAR, radar, and HD map data. Responsibilities include model architecture, large-scale distributed training, data pipelines, robustness evaluation, sensor fusion, research into foundation models, and mentoring ML engineers. The role requires deep expertise in 3D vision, autonomous systems, multi-view fusion, and production-scale machine learning.

Develop and deploy production machine learning models and sensor-fusion algorithms for autonomous-truck ego-motion estimation and localization. Build scalable PyTorch training workflows, analyze large vehicle datasets, and create robust C++ and Python production software. Define validation strategies, optimize performance under real-time constraints, make architecture decisions, and collaborate across autonomous-driving teams. Provide technical leadership through design reviews, code reviews, mentoring, and engineering best practices.

The Director of Engineering will lead the TorcOS team in developing embedded software for autonomous driving platforms, focusing on processes, team development, and cross-functional collaboration.

Design, develop, and deploy production 3D and BEV perception ML models for autonomous trucks. Own end-to-end model lifecycle from data prep, training, evaluation, and deployment. Improve model robustness, performance, and generalization across sensor modalities. Integrate models with autonomy software, enhance training pipelines and tooling, analyze failures, and mentor junior engineers.

Serve as a strategic HR partner to leaders, advising on talent management, performance, org design, employee relations, investigations, succession planning, compensation, change management, and HR analytics to align people strategies with business goals.