Autonomous systems for heavy construction equipment
Bedrock Robotics builds autonomy software for existing construction machinery, targeting the labor-shortage and safety gaps in heavy equipment operation. The tech stack is heavily weighted toward embedded systems (Rust, C++, CUDA, Lidar, PREEMPT_RT) and simulation (PyTorch, TensorFlow, JAX, distributed systems frameworks), with a modern DevOps and frontend layer (TypeScript, React, Electron, Tauri). Nearly all hiring (26 of 28 roles) is engineering-focused across senior and mid-level bands, with active projects spanning real-time fleet control, autonomous performance measurement, and cloud simulation pipelines — a profile consistent with a robotics company in active deployment phase.
Bedrock Robotics retrofits autonomous control systems onto existing heavy construction equipment, enabling fully autonomous operation while maintaining expert-level quality and safety margins. Founded in 2024, the company addresses a structural labor shortage in construction by automating excavators and other machinery in deployed field environments. Their platform integrates real-time telemetry (Lidar, video streams), embedded compute (NVIDIA Jetson, Rust runtime), and cloud-based simulation and monitoring. Operations span fleet management interfaces, performance evaluation systems, and cloud execution pipelines to coordinate multi-machine job sites. The company is headquartered in San Francisco and operates in the United States.
Embedded systems: Rust, C++, CUDA, PREEMPT_RT, NVIDIA Jetson, Lidar, FlexRay. ML/simulation: PyTorch, TensorFlow, JAX. Frontend: TypeScript, React, Electron, Tauri, WebGL, WebGPU. DevOps: systemd, udev, Linux. Monitoring: Foxglove.
Autonomous control systems for construction equipment. Active projects include fleet interfaces, autonomous excavator deployment, performance evaluation, simulation automation, and a cloud platform for coordinating and monitoring multi-machine job sites.
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Bedrock Robotics's technology stack, projects, and hiring signals are inferred from public hiring and company data — career pages, public listings, and company web presence — then clustered and de-duplicated. Figures are estimates that refresh over time. Read our full methodology →
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