Internship 2027 Onboard Infrastructure Engineer

Responsibilities

• Integrate open-source and proprietary LLM and VLA models into the onboard Rust middleware stack alongside perception, planning, and control pipelines.
• Profile and optimize model execution using TensorRT, vLLM, ExecuTorch, and custom edge inference runtimes for NVIDIA Jetson Thor.
• Streamline camera and LiDAR sensor tokenization for real-time model input without latency spikes in vehicle control loops.
• Identify and eliminate memory bandwidth, compute, and IPC bottlenecks using Nsight Systems, Nsight Compute, and eBPF.
• Validate performance optimizations directly on autonomous heavy machinery at test sites.

Requirements

• Currently pursuing a BS, MS, or PhD in Computer Science, Electrical or Computer Engineering, Robotics, or a related field.
• Proficiency in Rust or C++, with supporting experience in PyTorch or JAX.
• Solid foundation in GPU architectures, CUDA, or parallel computing.
• Understanding of multithreading, operating-system and GPU scheduling, memory management, asynchronous programming, and IPC.
• Practical experience with TensorRT, ONNX Runtime, or ExecuTorch on constrained hardware is a bonus.
• Experience with LLM/VLA optimization techniques such as KV-cache management, FP8/INT4 quantization, continuous batching, or speculative decoding is a bonus.
• Exposure to multimodal/VLA models or robotics frameworks is a bonus.