• Lead the development and optimization of ML inference pipelines on embedded hardware, including model conversion (PyTorch/ONNX), TensorRT integration, and CUDA-based pre/post-processing. • Design, review, and guide development of custom CUDA kernels to support proprietary model layers and performance-critical operations. • Drive technical execution across model optimization, inference scalability, benchmarking, and real-time system integration. • Ensure high-quality C++ and CUDA code through robust design, documentation, and test coverage. • Integrate optimized models and processing stages into Torc’s Application Engine and support Virtual Driver teams in adopting the optimized inference layer. • Hire, lead, and develop a high-performance engineering team, building a culture of ownership, collaboration, and continuous improvement. • Set technical and operational goals aligned with company-wide objectives; define and track team KPIs and milestones. • Provide coaching, career development, and skill-building opportunities for engineers; maintain development plans and performance expectations. • Establish and improve engineering processes for planning, delivery, testing, documentation, and cross-team collaboration. • Reinforce Torc’s values through transparent communication, conflict resolution, and proactive change leadership.