Senior, Engineering Manager - Perception & Auto-Tagging

Torc Robotics • Ann Arbor, MI

5d

About The Position

You'll lead the Offline Perception team within Data Loop and Simulation. The team explores the advantages of offline processing — building auto-labeling pipelines that combine different models and data-processing approaches to produce multi-modal, high-quality training data (2D, 3D, semantic segmentation, dense depth, and related modalities) that feeds online perception model development across Torc. You'll own strategy and execution for this data-to-model path, partnering with auto-tagging, data delivery, human annotation, and autonomy stakeholders on priorities, quality, and delivery.

Requirements

• BS or MS in Computer Science, Robotics, Electrical Engineering, or a related field, plus 8+ years of software or ML engineering experience, including 2+ years managing engineers.
• Hands-on experience with auto-labeling for self-driving development — not just model research, but pipelines that produce usable labels at scale.
• Track record delivering large-scale perception datasets in production contexts — e.g. 2D OD, 3D OD, semantic segmentation, dense depth, and related modalities — with clear quality and delivery ownership.
• Breadth across perception models and modalities — practical familiarity with different model families, what each is good for, and where they break down (accuracy, scale, latency, annotation cost, generalization).
• Experience with AI infrastructure and data pipelines — distributed compute and data platforms (e.g. Databricks, Ray, Spark), cloud infrastructure (e.g. AWS), and the tooling needed to train and evaluate at fleet scale.
• Proven track record leading and scaling technical teams, including hiring, mentoring, and performance management.
• Experience setting technical strategy and roadmap for ML or data engineering teams, and communicating tradeoffs to senior leadership.
• Strong communication skills, with the ability to represent team strategy to leadership and peer engineering teams.

Nice To Haves

• 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.

Responsibilities

• Lead, grow, and set technical direction for the Offline Perception team — hiring, mentoring, and managing the performance and career growth of engineers and ML engineers.
• Own the roadmap for offline perception model development and evaluation, from curated datasets through training, benchmarking, and delivery to downstream consumers.
• Stay technical enough to review and guide architecture decisions across auto-labeling integration, offline perception training and evaluation, and large-scale data pipeline design.
• Represent the team to senior leadership and cross-functional stakeholders in perception, simulation, and systems — translating strategy into concrete execution plans.
• Partner with product and peer engineering leaders (including auto-tagging, data delivery, and human annotation) to prioritize the roadmap and resolve cross-team dependencies.
• Drive process and quality improvements across the offline perception lifecycle — dataset specs, evaluation methodology, and repeatable delivery of production-grade training data.
• Manage team health and staffing, balancing headcount and skill mix between ML/perception engineering and data pipeline / AI infrastructure needs.

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
• Sign-on payments
• Relocation assistance

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Associate degree