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Leads development of BEV and multi-modal perception models for autonomous trucks, combining camera, LiDAR, radar, and HD map data. Responsibilities include model architecture, large-scale distributed training, data pipelines, robustness evaluation, sensor fusion, research into foundation models, and mentoring ML engineers. The role requires deep expertise in 3D vision, autonomous systems, multi-view fusion, and production-scale machine learning.

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.

Provide on-call MLOps support for machine learning teams by triaging pipeline issues, debugging workflows, identifying root causes, and maintaining reliable model development operations. Collaborate with senior engineers to improve tooling, documentation, and processes, while communicating technical findings across teams. The role requires Python, foundational PyTorch and model pipeline experience, plus familiarity with CI/CD, monitoring, logging, and orchestration tools.

Administer post-execution contract lifecycle and maintain the contract management system. Track milestones, deliverables, renewals, and amendments; support Procurement and Legal with contract interpretation, reporting, audits, SOPs, and database management to ensure compliance and accurate, audit-ready records.

Lead cloud cost management and FinOps activities across multi-cloud environments: cost analysis, budgeting, forecasting, tagging and allocation, rate optimization, anomaly/root-cause analysis, billing data aggregation, dashboards and showback/chargeback models, and cross-functional cost governance using FinOps best practices and Lean-Agile methods.

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.

Build and operate internal platform infrastructure supporting software and hardware development, testing, deployment, and release workflows for autonomous vehicles. Develop shared services for workflow orchestration, observability, remote development, authentication, and multimodal test execution. Provide incident response and internal customer support, improve reliability and scalability, troubleshoot complex issues, establish engineering standards, and mentor other engineers.

Build and maintain MLOps pipelines that convert, compile, benchmark, validate, and release machine learning models for autonomous truck edge hardware. Maintain model release registries, ensure version traceability and reproducibility, compare latency and accuracy across platforms, and collaborate with perception and safety teams to meet strict production requirements.

Perform operational safety analysis for autonomous heavy-truck testing, develop safety concepts, requirements, processes, procedures, metrics, and risk mitigation plans. Review designs, specifications, software, test plans, and reports for safety impacts; support incident investigations, safety cases, compliance, and readiness reviews. Collaborate across engineering, product, regulatory, service, maintenance, and fleet operations teams to ensure safe autonomous vehicle development and public-road testing.

Lead a team of release managers to coordinate and execute software and system releases for autonomous driving products. Oversee release trains, impact analysis, Git branching/tagging, configuration management, release artifacts, cross-team coordination, resource planning, risk mitigation, and people management to deliver production-quality releases.