AV Software Engineer

This is a fully remote position, open to applicants in California.

📋 Description

• Optimize and implement neural network models for onboard inference utilizing TensorRT and custom CUDA kernels.

• Analyze and minimize end-to-end latency throughout the autonomous driving system—from sensor data ingestion to control mechanisms.

• Develop and sustain the onboard software infrastructure in C++ and Rust, encompassing real-time data pipelines, inter-process communication, and hardware abstraction layers.

• Apply model quantization, pruning, and various optimization strategies to enhance throughput on embedded GPU platforms.

• Collaborate with machine learning engineers to ensure models are effectively designed for deployment, and with vehicle systems engineers to adhere to real-time safety requirements.

⛳️ Requirements

• BS, MS, or PhD in Computer Science, Electrical Engineering, Robotics, or a related discipline—or equivalent professional experience.

• Strong expertise in C++ and/or Rust for systems requiring high performance.

• Practical experience with GPU-accelerated computing—CUDA, TensorRT, or equivalent inference optimization frameworks.

• Knowledge of ML model architectures (such as transformers and CNNs) with the capability to assess computational costs and memory usage.

• Must be eligible to work in the United States.

• Background in onboard software for autonomous vehicles, robotics, or IoT/edge devices.

• In-depth understanding of CUDA, TensorRT, model quantization, and kernel optimization techniques.

• Experience with Bazel or similar build systems for complex codebases.

• Familiarity with real-time robotic systems.

• Proven experience in profiling and optimizing overall system performance (CPU, GPU, memory, I/O) on embedded platforms.

• Ability to thrive as an early team member—demonstrating high ownership, a low ego, and rapid iteration.

🏝️ Benefits

• This position offers a base salary along with benefits and equity compensation. Salary ranges are established by role, level, and geographical location. Individual pay within the range is influenced by additional factors such as qualifications, skills, experience, and location.