Senior, Engineering Manager

About the role

This role owns the data-to-model path within Data Loop and Simulation that produces high-quality training data for automated truck software. Success here advances autonomous freight technology inside a Daimler-backed organization.

Software engineers turn product ideas into working code. Engineers work in small teams, review each other's work, and ship in small batches. Most teams follow agile practices such as sprints and daily standups. Engineers also write tests, fix bugs, and improve performance. The field values clear communication as much as technical skill. Engineers spend part of every week on planning, code review, and debugging, not just writing new code. The ability to explain a technical decision in plain words separates strong engineers from the rest.

Key facts

What you'll do

Production and operations teams use perception, simulation, and systems to translate strategy into concrete execution plans for freight applications. Clear communication aligns cross-functional priorities across engineering and operations for autonomous truck workflows.

Collaboration balances quality, throughput, and feasibility for large-scale fleet data.

This work ensures the team delivers reliable data that trains downstream perception models for automated trucks.

Staffing decisions sustain delivery cadence and long-term scalability.

Requirements

A BS or MS in Computer Science, Robotics, Electrical Engineering, or a related field is required for this position. The degree supports foundational knowledge for perception and data systems in autonomous vehicles.

8+ years of software or ML engineering experience, including 2+ years managing engineers, is required as a baseline for leadership in this role. This experience ensures readiness for managing complex technical workflows.

Hands-on experience with auto-labeling for self-driving development, beyond model research, including pipelines that produce usable labels at scale, is essential for building reliable training data for truck autonomy.

A track record delivering large-scale perception datasets in production contexts, such as 2D OD, 3D OD, semantic segmentation, dense depth, and related modalities, with clear quality and delivery ownership, validates capability in production-critical environments.

Breadth across perception models and modalities, with practical familiarity with different model families, their strengths, and their limitations in accuracy, scale, latency, annotation cost, and generalization, supports informed roadmap and tradeoff decisions.

Experience with AI infrastructure and data pipelines, including distributed compute and data platforms like Databricks, Ray, and Spark, plus cloud infrastructure such as AWS, enables fleet-scale training and evaluation workflows.

The ability to lead and scale technical teams, including hiring, mentoring, and performance management, creates conditions for consistent, high-quality output.

Setting technical strategy and roadmap for ML or data engineering teams and communicating tradeoffs to senior leadership aligns daily work with business outcomes.

Strong communication skills to represent team strategy to leadership and peer engineering teams keep stakeholders informed and aligned.

Nice to have

Experience leading ML or data engineering teams in autonomous vehicles or robotics.

Familiarity with VLMs, auto-labeling pipelines, or perception model evaluation methodology.

Experience managing managers or leading multiple teams simultaneously.

Familiarity with scenario description standards such as Pegasus layers.

Practical notes

This role may involve US-based remote work with flexibility and occasional onsite presence as needed.

The position requires adherence to security and compliance standards for freight and automated driving data.

Good to know

The role centers on perception and data pipelines for autonomous truck software.

Common tools in this field include data platforms, cloud infrastructure, and ML frameworks.

Engineering managers balance people leadership, technical depth, and cross-team coordination.

Scenario and data standards support consistency across large, multi-modal datasets.

The position contributes directly to freight automation within a commercial vehicle context.

About the company

Torc Robotics writes the software that lets heavy trucks drive themselves on public highways. The company began in Blacksburg, Virginia, in 2005 with students from Virginia Tech and later became part of Daimler Truck. Engineering and test work now run in the United States, Canada, and Germany, with an early commercial focus on Freightliner Cascadia trucks used in U.S. freight.