Onboard AV Software Engineer

Job description

## About Humble Robotics Working at Humble Robotics means taking on the biggest change in ground transportation in decades. We’re building an autonomous, zero-emissions hauler that dramatically lowers the cost of freight with groundbreaking vision-based AI, designed for today’s global logistics network. We’re a fast-moving, close-knit team of AV industry veterans and innovative thinkers. We don’t believe culture can be engineered – but when it falls into place, it’s a once-in-a-lifetime adventure. *Progress has never felt so present.* ### Position Overview We're looking for a software engineer to optimize and deploy ML models on our trucks' onboard compute, and to own performance across the full autonomous driving stack. You'll take models from our ML team and make them run fast, efficiently, and reliably on embedded GPUs—using TensorRT, custom CUDA kernels, and low-level systems engineering. Beyond inference, you'll profile and optimize the entire onboard software pipeline to meet hard real-time deadlines. This is a rare chance to bridge ML and embedded systems for production autonomous freight, with the freedom and responsibility that comes with a small team tackling a massive problem. ### Key Responsibilities - Optimize and deploy neural network models for onboard inference using TensorRT and custom CUDA kernels - Profile and reduce end-to-end latency across the autonomous driving stack—from sensor ingestion to control - Build and maintain the onboard C++ and Rust software infrastructure, including real-time data pipelines, inter-process communication, and hardware abstraction layers - Implement model quantization, pruning, and other optimization techniques to maximize throughput on embedded GPU platforms - Collaborate with ML engineers to ensure models are designed for efficient deployment, and with vehicle systems engineers to meet real-time safety constraints ### Minimum Qualifications - BS, MS, or PhD in Computer Science, Electrical Engineering, Robotics, or a related field—or equivalent industry experience - Strong proficiency in C++ and/or Rust for performance-critical systems - Hands-on experience with GPU-accelerated computing—CUDA, TensorRT, or similar inference optimization toolchains - Familiarity with ML model architectures (transformers, CNNs) and the ability to reason about computational cost and memory footprint - Eligible to work in the United States - Experience with onboard software for autonomous vehicles, robotics, or IoT/edge devices - Deep knowledge of CUDA, TensorRT, model quantization, and kernel-level optimization - Experience with Bazel or similar build systems for complex codebases - Familiarity with real-time robotic systems - Experience profiling and optimizing full-system performance (CPU, GPU, memory, I/O) on embedded platforms - Comfort operating as an early team member—high ownership, low ego, fast iteration ### Compensation This role is eligible for base salary \+ benefits \+ equity compensation. Salary ranges are determined by role, level, and location. Within the range, individual pay is determined by additional factors, including qualifications, skills, experience, and location. ### Additional Information As part of the interview process, we may use Artificial Intelligence (AI) tools to compare your qualifications and experience to the job description. A human reviews all AI output and makes a final hiring decision. Humble Robotics does not rely on the output to make any employment decisions. Some applicants may have a legal right to opt-out of the use of AI as part of our interview process. Contact \*\*legal@humblerobotics.ai\*\* to exercise this right or if you have further questions on the use of AI tools in our hiring process. Humble Robotics is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, national origin, gender, age, religion, disability, sexual orientation, veteran status, marital status or any other characteristics protected by law. Humble Robotics will consider qualified applicants with arrest and conviction records in a manner consistent with local ordinances.