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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.

Lead design and deployment of offline 3D reconstruction, lane-line detection, and automatic mapping pipelines from multi-sensor logged data. Develop pose estimation, sensor fusion, and perception models; implement MLOps, data ingestion/curation, and quality measurement. Serve as project lead, mentor engineers, and define analytics and ML standards to scale auto-labeling and map creation.

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 a release pipelines team building automation to produce cohesive releases of safety-critical embedded software. Own build automation, high-speed build systems (Bazel, BuildStream), workflow automation (Temporal), packaging performance, release candidacy workflows, and integrations with tracking systems and hardware test platforms. Define roadmap for build infrastructure, ensure secure Debian-based distribution and containerized delivery, and hire/develop engineers.

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, implement, test, and maintain scalable C++ software for autonomous vehicle and perception systems. Work across full SDLC, support vehicle testing, simulation-based regression, data acquisition/analysis from remote vehicles, root-cause analysis, and mentor engineers while collaborating with ML researchers and cross-functional teams.

Develop and own high-fidelity vehicle dynamics models and simulation platforms for autonomous truck validation. Collaborate with autonomy teams to capture requirements, run automated regression and validation pipelines, perform root-cause analysis including HIL, and support large-scale RL training and system-level V&V.

The software engineer will develop and maintain Linux software for autonomous vehicles, optimize performance, debug systems, and ensure system stability and security, collaborating with cross-functional teams.

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

Designs and executes vehicle- and software-level test plans for autonomous trucks, collects and analyzes test data, performs root-cause analysis, implements data acquisition and automation systems, supports CI/CD and software development in Linux (C++ and scripting), and conducts on-road testing and reporting to improve system safety and performance.