Operations - Quality Engineering Lead

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. Preferred Qualifications Bachelor’s or Master’s degree in Engineering (Mechanical, Electrical, Systems, or related field) 8+ years of experience in product quality, manufacturing quality, or reliability engineering—preferably in component level, system hardware, robotics and/or autonomous systems Proven experience across NPI phases (EVT, DVT, PVT, ramp, sustaining) Deep expertise in quality systems: FMEA, SPC, CAPA, 8D, Six Sigma methodologies Hands-on experience overseeing quality processes enabling checks and balances, developing supplier with “quality first” mindset Strong background in reliability engineering and failure analysis Experience with fleet-based or deployed systems is highly preferred If you thrive in dynamic environments, love solving complex challenges, and want to make an impact with cutting-edge technology, we’d love to hear from you! Day in the life - QE You spend your day turning messy, cross-functional problems into structured insights and scalable fixes—so the product works reliably at scale. Morning: Data, Triage, and Alignment You usually start by reviewing dashboards and overnight reports: Fleet health metrics (uptime, failure rates, MTBF/MTTR) Manufacturing yield and defect paretos from the previous build Any critical field escalations or production line stops If something is off—say a spike in failures in a subsystem—you’ll quickly prioritize it for deeper investigation. From there, you jump into a cross-functional stand-up with hardware, software, manufacturing, and operations: Align on top quality risks across EVT/DVT/PVT or production builds Review open issues, owners, and timelines Decide where escalation or additional resources are needed You’re setting the tone: what matters today, and what cannot slip.   Mid-Morning: Deep Dive on Issues This is where you spend focused time on root cause analysis. Example scenarios: A recurring field failure in the robotic fleet → you’re reviewing logs, diagnostic data, and failure modes with the firmware and systems teams A yield drop at a contract manufacturer → digging into process changes, supplier variation, or test coverage gaps A reliability test failure → working with engineering to understand whether it’s a design limitation or test artifact You’re not just asking “what failed”—you’re pushing toward: Root cause clarity (not symptoms) Containment actions (what do we do now?) Permanent fixes (design, process, or supplier changes)   Midday: Factory / Lab / Field Interface Depending on the phase of the program, this block varies: During NPI builds (EVT/DVT/PVT): You’re on the line (physically or virtually), reviewing: First pass yield Assembly issues (DFA gaps) Test coverage and escapes During production: Reviewing IQC/IPQC/OQC trends Syncing with supplier quality teams on incoming defects Auditing process controls and corrective actions For fleet operations: Meeting with field ops on triage trends Reviewing top downtime drivers Prioritizing fixes that impact uptime and serviceability   Afternoon: Systems, Process, and Scaling This is where you zoom out from individual issues to system-level improvements. You might be: Building or refining quality dashboards and KPIs Defining new quality gates for upcoming builds or releases Improving diagnostic frameworks so failures are easier to detect and classify Working on automated test strategies (production or validation) This is also where AI and tooling come in: Setting up automated anomaly detection on fleet or manufacturing data Streamlining how issues are categorized and routed Reducing manual triage work through smarter workflows The goal: make the system smarter so the same problems don’t repeat.   Late Afternoon: Cross-Functional Reviews & Decisions You’ll often close the day in decision-making forums: Design reviews (pushing for DFM/DFA improvements) Quality reviews with leadership (status, risks, mitigation plans) Supplier calls for escalations or performance management This is where you influence: Whether a build proceeds or is gated Whether a design is ready for the next phase Where the team invests time and resources   End of Day: Synthesis & Prioritization Before wrapping up, you: Reassess top risks across product, manufacturing, and fleet Ensure owners and timelines are clear for critical issues Prepare concise updates for leadership (what’s broken, what’s improving, what’s at risk)   What Makes the Role Unique You’re one of the few roles that sees the full picture —design → factory → field You operate at both microscopic level (root cause) and system level (process + metrics) You constantly balance speed vs. quality in a fast-moving hardware environment No two days are the same—priorities shift based on real-world data Our roles are often flexible. If you don't fit all the criteria, or are in another location (especially one where we have an office like SF or NY) please apply anyway! We'd love to consider you.

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