Engineering Manager, Active Sensors (LiDAR)

About the Company

At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business.

A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners.  Now a part of the Daimler family , we are focused solely on developing software for automated trucks to transform how the world moves freight.

Join us and catapult your career with the company that helped pioneer autonomous technology, and the first AV software company with the vision to partner directly with a truck manufacturer.

Meet the team

Torc’s Perception teams develop the production machine learning systems that enable our autonomous trucks to understand their surroundings. The Active Sensors team owns the end-to-end lidar perception pipeline, from motion compensation and point-cloud aggregation to multitask learned perception for object detection, road and lane understanding, 3D occupancy estimation for safe vehicle operations. These systems must run in real-time on embedded compute while remaining robust to adverse weather, sensor degradation, and sensor failures. The team works across machine learning, 3D perception, and embedded systems, balancing perception performance and robustness against latency, memory, power and on-board compute budget.

As the team’s Engineering Manager, you will lead development across the complete perception lifecycle, from data requirements and pipeline architecture through model training, evaluation, integration, deployment, and robotic testing. You will collaborate closely with teams across sensor hardware, data, infrastructure, simulation, compute platform, systems engineering, safety, perception, and planning.

What you'll do:

The Perception department is seeking an experienced and technically strong Engineering Manager to lead a machine learning team developing active sensor-based perception capabilities for our autonomous trucks.

You will be responsible for the team’s people leadership, technical direction, and execution. You will create an environment in which engineers can deliver high-quality results, advance the technology, and grow both individually and as a team. You will own roadmap planning, prioritization, and delivery while ensuring that the team’s commitments are clear, realistic, and aligned with the company milestones.

You will work within a broader organization of teams developing perception and behavior capabilities across the autonomous-driving stack. You will represent the team across the organization, collaborate with technical and cross-functional partners, and communicate progress, dependencies, and risks to senior leadership.

The role requires a leader who combines strong people management with technical depth and disciplined execution. You will remain sufficiently close to the team’s work to guide technical decisions and remove blockers while connecting its roadmap to the department’s broader mission of delivering production-grade machine learning systems for autonomous trucking.

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 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 that 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.