Manager, Engineering - App Engine (CUDA)

About the Company

At Torc, we have always believed that autonomous vehicle technology will transform how we travel, move freight, and do business. A leader in autonomous driving since 2007, Torc has spent over a decade commercializing our solutions with experienced partners. We are focused solely on developing software for automated trucks to transform how the world moves freight.

Meet the Team

The Application Engine team builds the middleware platform that powers the next generation of Torc’s Level 4 autonomous trucking stack. Our mission is to provide a robust, efficient, and flexible environment for integrating and managing diverse deep learning models and processes. We enable scalable development workflows, consistent performance, and safety-compliant deployments across real-world and simulated environments. By owning the core application layer, the App Engine team provides the scaffolding that allows feature teams to efficiently develop, integrate, and deploy advanced autonomous driving capabilities. We partner closely with Perception, Planning, Systems, Validation, Hardware, and Safety to ensure reliability, determinism, and scalability in production.

About the Role

We are seeking a technically strong and people-focused Engineering Manager to lead our App Engine team. While this is not a hands-on coding role, deep technical expertise in GPU parallel computing, CUDA, and model deployment is essential. You will drive technical direction, prioritize execution, and grow a high-performing team responsible for building production-grade middleware. This role sits at the intersection of software engineering, ML frameworks, GPU optimization, and safety-critical integration. Success requires both the ability to lead and coach engineers, and the technical depth to guide design decisions and challenge assumptions.

What You’ll Do

• 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.

What You’ll Need to Succeed

• 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.