Bedrock Robotics develops autonomous construction technology that retrofits heavy equipment to operate as coordinated, driver-less fleets.
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 is composed of industry 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 machinery across the country, accelerating project schedules of billion-dollar infrastructure projects and improving safety on job sites. Backed by $350M in funding, we’re working quickly to close the gap between America's surging demand for housing, data centers, manufacturing hubs, and the construction industry's growing labor shortage. This is where algorithms meet steel-toed boots. You’ll collaborate with construction veterans and world-class engineers to solve physical-world problems that simulations can’t touch. If you're ready to apply cutting-edge technology to solve meaningful problems alongside a talented team—we'd love to have you join us. Role Overview: We are seeking a Product Quality Engineer to establish and drive end-to-end quality strategy across hardware development and robotic fleet operations. This role sits at the intersection of engineering, manufacturing, and field operations, ensuring product reliability, scalability, and performance from early prototyping through high-volume production and real-world deployment. You will own quality systems, metrics, and continuous improvement initiatives across the full lifecycle—spanning design, supplier quality, manufacturing execution, and field performance—while leveraging modern tools including automation, diagnostics, and AI-driven workflows. KEY RESPONSIBILITIES: Product & Design Quality - Lead Design for Quality initiatives, including Design for Manufacturing (DFM) and Design for Assembly (DFA) - Partner with hardware, electrical, and software engineering teams during EVT/DVT/PVT phases to ensure robust design validation - Define and enforce quality gates, risk assessments (FMEA), and validation plans - Drive design improvements based on failure data, field insights, and reliability testing - Risk Management: Proactively identify and mitigate business-critical risks and dependencies that may impact delivery or operational performance. Develop contingency plans as necessary and maintain visibility to the relevant company functions of major issues and alerts. Manufacturing & Supplier Quality - Establish and manage Incoming Quality Control (IQC), In-Process Quality Control (IPQC), and Outgoing Quality Control (OQC) frameworks - Develop supplier quality strategy, including qualification, audits, and performance management - Implement process controls, yield tracking, and defect reduction initiatives across contract manufacturers - Lead root cause analysis and corrective/preventative actions (CAPA) for production issues Reliability & Validation - Define reliability requirements and test strategies (HALT/HASS, environmental, lifecycle testing) - Own validation metrics and ensure products meet performance and durability targets - Drive continuous reliability improvements through structured failure analysis Fleet Quality & Field Operations - Establish systems for field triage, failure tracking, and escalation management across deployed robotic fleets - Analyze field performance data to identify systemic issues and prioritize fixes - Partner with operations and service teams to improve uptime, serviceability, and MTBF - Develop feedback loops from field → engineering → manufacturing Software & Systems Quality - Ensure alignment between hardware and software quality standards - Define test strategies for embedded systems, firmware, and cloud-connected platforms - Drive automated testing frameworks (HIL/SIL), regression testing, and release quality metrics Data, Metrics & Continuous Improvement - Define and track KPIs across the lifecycle: yield, defect rates, DPPM, MTBF, MTTR, fleet uptime - Build dashboards and reporting systems to provide visibility across engineering, operations, and leadership - Lead structured problem-solving using 8D, 5 Whys, Fishbone, and statistical methods Automation, Diagnostics & AI Enablement - Build diagnostic and alert frameworks for rapid issue identification and communication across hardware and software systems - Leverage AI agents and tools to: - Streamline root cause analysis and data triage - Automate reporting, anomaly detection, and workflow management - Improve cross-functional coordination across complex, multi-phase programs KEY REQUIREMENTS: - Experience: 8-10+ years in quality program management or a similar role, with proven experience managing complex, cross-functional projects in fast-paced, tech-driven environments. - Technical Proficiency: Strong understanding of quality concepts and the ability to communicate complex ideas across diverse teams. Experience with robotics, automation, or key hardware-related areas such as compute, memory design and utilization is a plus. - Problem-Solving Mindset: Ability to manage and resolve complex challenges with little to no established playbooks, using creative and proven strategic thinking to drive solutions. - Cross-Functional Leadership: Demonstrated ability to work effectively across diverse teams (Engineering, Product, Operations, Partnerships, etc.). - Communication Skills: Exceptional verbal and written communication skills. Comfort in presenting Metrics and Quality approaches to senior leadership and external stakeholders. - Risk Management: Proven track record of identifying, managing, and mitigating risks in large, complex programs. - Adaptability: Comfortable with ambiguity and able to thrive in a fast-moving, constantly evolving environment. - Travel: Willing to travel domestically and internationally up to 20% PREFERRED QUALIFICATIONS: - Bachelor’s or Master’s degree in Engineering