Engineering Manager, Active Sensors (LiDAR)

Torc's Active Sensors team owns the end-to-end lidar perception pipeline for autonomous trucks, from motion compensation and point-cloud aggregation to multitask learned perception. The Engineering Manager leads the team's people leadership, technical direction, roadmap, and delivery, and collaborates across sensor hardware, data, simulation, embedded platforms, planning, and safety teams.

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 that improvements to one task do not introduce unacceptable regressions in other tasks or in downstream system behavior; Define the team's 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 that 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; Review technical designs, model architectures, experimental results, training artifacts, and verification evidence; 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

Requirements

Bachelor's degree in Computer Science, Robotics, Electrical Engineering, or related field with 6+ years of professional experience, or a master's degree with 4+ years of experience; 2+ years of experience leading and managing engineers, including coaching, performance management, and career development; 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 with one or more relevant areas, such as multitask learning, object detection, road and lane detection, or 3D occupancy estimation; 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; Strong understanding of embedded computing platforms and the 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 both technical teams and senior leadership

Nice to have

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, NPP, developing custom TensorRT operations; Publications, patents, or open-source contributions in machine learning, computer vision, robotics, or autonomous driving

Benefits

A competitive compensation package that includes a bonus component and stock options; 100% paid medical, dental, and vision premiums for full-time employees; 401K plan with a 6% employer match; Flexibility in schedule and generous paid vacation (available immediately after start date); Company-wide holiday office closures; AD&D and Life Insurance