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Develop and deploy production machine learning models and sensor-fusion algorithms for autonomous-truck ego-motion estimation and localization. Build scalable PyTorch training workflows, analyze large vehicle datasets, and create robust C++ and Python production software. Define validation strategies, optimize performance under real-time constraints, make architecture decisions, and collaborate across autonomous-driving teams. Provide technical leadership through design reviews, code reviews, mentoring, and engineering best practices.

The Director of Engineering will lead the TorcOS team in developing embedded software for autonomous driving platforms, focusing on processes, team development, and cross-functional collaboration.

Design, develop, and deploy production 3D and BEV perception ML models for autonomous trucks. Own end-to-end model lifecycle from data prep, training, evaluation, and deployment. Improve model robustness, performance, and generalization across sensor modalities. Integrate models with autonomy software, enhance training pipelines and tooling, analyze failures, and mentor junior engineers.

Serve as a strategic HR partner to leaders, advising on talent management, performance, org design, employee relations, investigations, succession planning, compensation, change management, and HR analytics to align people strategies with business goals.

Develop and deliver production-ready 3D and camera-based perception ML models (BEV emphasis). Own model training, evaluation, debugging, and integration into autonomy systems. Improve training pipelines, tooling, and robustness; lead small technical initiatives and mentor junior ML engineers while collaborating cross-functionally to deploy perception models at scale.

Lead product cybersecurity architecture for autonomous vehicle systems: define and evaluate controls (OS, networking, cryptography), conduct threat modeling and verification activities, embed security into development lifecycles, liaise with vendors, and present cybersecurity evidence for product releases.

Build, deploy, and operate large-scale automated event-tagging and data pipelines for autonomous vehicle logs. Own CI/CD with GitHub Actions, Databricks processing, and AWS infrastructure as code. Integrate and productionize ML tagging models, monitor data quality and pipeline health, and troubleshoot performance, reliability, and cost at scale.

Lead architecture and implementation of motion planning, trajectory generation, and optimization for autonomous trucks. Provide multi-team technical leadership, design production-quality C++ planning software, integrate with controls and perception, validate via SiL/HiL/ViL, and support on-vehicle debugging, deployment, and performance optimization to deliver safe, reliable autonomy at scale.

Design and deploy object detection and tracking modules; develop ML algorithms; guide team members; improve data governance and quality assessment.

Embed with one or two Autonomy teams to enable large-scale replay/recompute workflows: implement data flows, onboard models, ensure metric evaluation and visualization adoption, debug full-stack issues, document workflows, and translate team feedback into simulation platform improvements.