Engineering Manager, Active Sensors (LiDAR)

Role overview

A leadership role for an engineering manager directing a machine learning team that builds active-sensor (LiDAR and radar) perception systems for autonomous trucks. The position blends people management with hands-on technical guidance across the full perception lifecycle, from data and architecture through model training, evaluation, deployment, and on-vehicle validation, ensuring models run reliably on embedded compute in real-world conditions. The successful candidate will own roadmap, prioritization, and delivery while partnering with cross-functional groups across sensor hardware, data infrastructure, simulation, compute platform, systems engineering, safety, and planning.

Responsibilities

- Lead, coach, and grow a team of ML and software engineers through hiring, performance management, career development, and continuous feedback - Set technical direction, roadmap, and priorities for active-sensor perception aligned with perception and vehicle-level milestones - Own end-to-end delivery of multitask models for object detection, road and lane detection, and free-space estimation using LiDAR and radar data - Guide architecture decisions spanning shared backbones, task-specific representations, sensor fusion, temporal modeling, and uncertainty estimation, while preventing regressions across tasks or downstream behavior - Define strategies that maintain perception quality under adverse weather, environmental change, sensor degradation, sensor failures, missing inputs, and unreliable or delayed data - Represent the team across the broader organization, communicating progress, dependencies, and risks to senior leadership and technical partners

Requirements

- Demonstrated success as an engineering manager leading teams that ship production machine learning systems - Deep expertise in 3D perception, multitask learning, and deploying real-time perception models on embedded compute under latency, memory, and power constraints - Hands-on understanding of the ML lifecycle, including data requirements, model development, experimentation, evaluation, integration, deployment, and robotic testing - Experience with pipelines covering motion compensation, point-cloud aggregation, sensor fusion, and graceful degradation behaviors - Track record of defining technical roadmaps and managing complex cross-functional dependencies against program milestones - Strong written and verbal communication skills, with the ability to explain technical decisions, tradeoffs, and risks to both engineering teams and senior leadership

Nice to have

- PhD in Computer Science, Robotics, Electrical Engineering, Machine Learning, or a related field - Hands-on experience with NVIDIA libraries and frameworks such as CUDA, cuDNN, cuBLAS, NPP, or building custom TensorRT operations - Publications, patents, or open-source contributions in machine learning, computer vision, robotics, or autonomous driving

Benefits and work setup

- Hybrid office-based work in Ann Arbor, MI; Blacksburg, VA; or Fort Worth, TX, with remote options also available within the United States - Competitive compensation package that includes a bonus component and equity (stock options) - 100% employer-paid medical, dental, and vision premiums for full-time employees - 401(k) plan with a 6% employer match - Flexible scheduling and generous paid vacation available from the start date, plus company-wide holiday office closures - AD+D and life insurance coverage