Lead and grow a team building the application framework that integrates deep learning models into Torc’s autonomy stack.
Drive technical execution across key focus areas: CUDA optimization, GPU resource management, model conversion pipelines (PyTorch, TensorRT, ONNX), and real‑time system integration.
Ensure reliability, determinism, and scalability across multi‑sensor autonomous driving workloads.
Partner with cross‑functional teams (Perception, Planning, Systems, Validation, Hardware, Safety) to align technical direction and integration priorities.
Mentor, coach, and develop engineers while cultivating a collaborative, high‑trust team culture.
Manage execution and delivery, balancing near‑term goals with long‑term architectural vision.
Establish scalable processes for development, testing, and integration in a safety‑critical environment.
Requirements
Bachelor’s or Master’s degree in Electrical Engineering, Computer Engineering, Robotics, or a related technical field.
3+ years of full‑cycle people management experience.
Deep technical expertise with CUDA, GPU parallel computing, and inference optimization.
Strong proficiency in C++ and Linux‑based development with knowledge of real‑time systems.
Experience with ML frameworks (PyTorch, TensorRT, ONNX) and model deployment pipelines.
Proven leadership managing software engineering teams in complex, cross‑functional environments.
Strong understanding of system‑level integration and safety‑critical requirements.
Ability to thrive in a fast‑paced, dynamic, and highly collaborative environment.