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Torc Robotics

Autonomous truck software for Level 4

Engineering Manager - Active Sensors, Lidar

Engineering LeadershipConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • A bachelor's degree in Computer Science, Robotics, Electrical Engineering, or a related field with 6+ years of professional experience, or a master's degree with 4+ years of experience.
  • At least 2 years of experience leading and managing engineers, including coaching, performance management, and career development.
  • A strong technical foundation in machine learning and computer vision, including 3D geometry, model evaluation, uncertainty, and perception failure modes.
  • Experience developing and deploying production machine learning systems for autonomous driving, robotics, or another real-world application.
  • Experience in one or more relevant areas, such as multitask learning, object detection, road and lane detection, or 3D occupancy estimation.
  • A strong understanding of lidar sensing and its impact on perception, including scan patterns, reflectance, FMCW lidar, and sensor time synchronization.
  • Experience across the machine learning lifecycle, including data curation, model training, controlled experimentation, offline evaluation, system integration, and production validation.
  • Experience analyzing data distributions, dataset coverage, long-tail scenarios, and the relationship between training data and model performance.
  • Strong proficiency in Python and PyTorch, along with practical experience using C++ in production perception or machine learning systems.
  • Experience deploying and optimizing deep learning models using TensorRT, including evaluating tradeoffs among model performance, robustness, latency, memory, power, and hardware utilization.
  • A strong understanding of embedded computing platforms and constraints associated with deploying real-time perception systems.
  • Experience defining technical roadmaps, planning complex machine learning projects, managing cross-functional dependencies, and delivering against program milestones.
  • Strong written and verbal communication skills, with the ability to explain technical decisions, results, tradeoffs, and risks to technical teams and senior leadership.

Responsibilities

  • Lead, coach, and develop a team of machine learning and software engineers, including hiring, performance management, career development, and continuous feedback.
  • Set the technical direction, roadmap, and priorities for active-sensor perception in alignment with perception and vehicle-level milestones.
  • Own the development and delivery of multitask models for object detection, road and lane detection, and free-space estimation using lidar and radar data.
  • Guide architecture and design decisions involving shared backbones, task-specific representations, sensor fusion, temporal modeling, uncertainty estimation, and interactions among perception tasks.
  • Ensure improvements to one task do not cause unacceptable regressions in other tasks or downstream system behavior.
  • Define the strategy for maintaining consistent perception performance across adverse weather, changing environmental conditions, sensor degradation, and sensor failures.
  • Drive model and system designs that support graceful degradation when sensor inputs are missing, degraded, delayed, or unreliable.
  • Own delivery across the machine learning lifecycle, including data requirements, model development, experimentation, evaluation, integration, release, and monitoring.
  • Ensure training and evaluation datasets provide sufficient quality and coverage across operating conditions, geographic features, rare events, adverse weather, and sensor-failure modes.
  • Establish rigorous task-level and system-level metrics, benchmarks, and failure-analysis practices covering precision, recall, range, latency, robustness, cross-task performance, and downstream impact.
  • Review technical designs, model architectures, experimental results, training artifacts, and verification evidence to ensure decisions are supported by data.
  • Collaborate with teams across multimodal perception, prediction and planning, data, infrastructure, simulation, sensor hardware, embedded platforms, systems engineering, and safety.
  • Track execution against commitments and communicate progress, risks, dependencies, and staffing needs to senior leadership.
  • Maintain high engineering standards through design reviews, code and model reviews, reproducible experimentation, and clear release-readiness criteria.
  • Guide technical decisions and remove blockers while connecting the team’s roadmap to the department’s mission of delivering production-grade machine learning systems for autonomous trucking.

Desired Qualifications

  • A PhD in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related field.
  • Experience with relevant NVIDIA libraries and frameworks, such as CUDA, CuDNN, CuBLAS, and NPP, or developing custom TensorRT operations.
  • Publications, patents, or open-source contributions in machine learning, computer vision, robotics, or autonomous driving.

About the Company

Torc Robotics

Autonomous truck software for Level 4

Torc Robotics builds software for autonomous trucks, aiming to enable Level 4 self-driving capabilities for freight transport. They develop driving systems that let heavy trucks operate autonomously under certain conditions, and they work with truck manufacturers and fleet operators to bring these solutions to market. The company integrates sensors from partners (such as Luminar Technologies) and provides ongoing updates and support to ensure reliability and performance. Unlike more general AV players, Torc focuses on commercial trucking and leverages strategic partnerships (for example with Daimler Trucks) to integrate its software into production vehicles and fleets. Its goal is to improve road safety and freight efficiency by deploying scalable, commercial autonomous trucking solutions.

Company size
501-1,000
Company stage
M&A
Total funding
Not listed
Headquarters
Blacksburg, VA
Founded
2005

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