Top Tech Jobs & Startup Jobs

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.