2027 Internship Onboard Infrastructure Engineer, ML Inference

Join the Team Bringing Advanced Autonomy to the Built World

At Bedrock, we're moving AI out of the lab and into the real world. Our team includes veterans who helped launch Waymo, scaled Segment to a $3.2B acquisition, and grew Uber Freight to $5B in revenue. Today, we're deploying autonomous systems on heavy construction equipment across the country, improving safety on job sites and accelerating schedules on critical infrastructure projects.

We're not here debating the future of AI. We're deploying it in the real world. In just two years, we've raised $350M and achieved the first fully autonomous excavator deployments in construction.

This is where algorithms meet steel-toed boots. You'll work alongside construction veterans and world-class engineers to solve physical-world problems that simulations can't touch. If you're ready to do meaningful work on hard problems, we'd love to have you join us.

About the Role & Team

The Onboard Infrastructure team builds the core engine of Bedrock’s autonomous heavy machinery - where safety, real-time control, and millisecond-level responsiveness are non-negotiable. As an Onboard Infrastructure Intern, you will bridge frontier AI and real-time execution by integrating Large Language Models (LLMs) and Vision-Language-Action (VLA) models into our core stack, ensuring multi-billion parameter multi-modal models run within strict, deterministic deadline budgets on edge compute.

What You'll Do

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

What We're Looking for

Required

• Currently pursuing a BS, MS, or PhD in Computer Science, Electrical/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 modern systems concepts: multithreading, OS and GPU scheduling, memory management, asynchronous programming and IPC.

Bonus Points

• Practical experience deploying neural networks on constrained hardware using TensorRT, ONNXRuntime or ExecuTorch.
• Experience with LLM/VLA optimization techniques such as KV-cache management, FP8/INT4 quantization, continuous batching or speculative decoding.
• Exposure to multi-modal/VLA models or robotics frameworks.