Manager, Engineering - Offline Perception

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.

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

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 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

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