Hardware Engineer, Autonomy Platforms

+ years of relevant industry experience preferred Heavy machinery hydraulic systems including electronic control High performance embedded compute, in particular Nvidia Jetson lineup High and low speed communication protocols (GMSL, FPDlink, PCIe, multi GigE, A2B, CAN, UART, SPI, RS232/422/485, etc…)

Experience with system engineering requirements definition and V&V High power LED lighting for scene illumination

Experience with common standards for HW deployment in construction ISO
26262, ISO
1675

• High power LED lighting for scene illumination
• Experience with common standards for HW deployment in construction
• Familiarity with large commercial construction equipment basic machine electrical and mechanical HW and interface
• Bedrock Robotics is an Equal Opportunity Employer [...] Bedrock Robotics is an Equal Opportunity Employer
• Reasonable Accommodations
• We want our hiring process to be accessible to everyone. If you need an accommodation to participate in the application or interview process, please let your recruiter know so we can support you.
• Remote operator stations including ergonomic human interfaces, low latency information display, and safety critical design
• HMI deployment in rugged (high shock/vibe and temp) environments including screens and/or tablets
• Packaging and integration strategies for high performance HW systems in rugged environments including high shock/vibe, wide temp ranges, IP, and solar loading
• High speed signaling including GSML, multi gigabit ethernet, RF
• Proficiency in compiled (C/C++/Rust), shell (linux bash), or runtime (Python) languages with ability to prototype and debug software components of HW systems as well as run tests, data collection, analysis, and reporting
• Preferred:
• + years of relevant industry experience preferred

Courses relevant to this posting

Relevance is based on skills and concepts, not a guarantee that you qualify.

Coming-soon courses matched to this posting: outline, waitlist, and skill path.

1. Operational Design Domains and Safety Architecture

Define where and how your autonomous system operates, and build the safety case that governs deployment decisions.

2. Sensor Fusion and Perception Pipelines

Combine camera, lidar, radar, and map data to build robust representations of the driving environment.

3. Prediction and Behavior Modeling

Forecast how other agents will move and reason about intent in multi-agent environments.

4. Motion Planning and Control

Generate safe, comfortable trajectories and translate them into vehicle control actions.

5. Simulation and Scenario-Based Testing

Use synthetic environments and scenario generation to validate system behavior before real-world deployment.

6. Data Loops and Continuous Improvement

Collect, label, and use fleet data to improve models and expand operating capabilities over time.

7. Deployment and Fleet Operations

Manage real-world rollout, remote assistance, cybersecurity, and operational scaling for autonomous fleets.