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San Francisco, California, United States
Onsite
At Bedrock, we're moving AI out of the lab and onto real job sites. Our team includes veterans who helped launch Waymo, scaled Segment to a $3.2B acquisition, and grew Uber Freight to $5B in revenue. We retrofit the heavy equipment contractors already own, starting with excavators, so experienced crews can safely take on more projects and deliver more of the critical infrastructure our society needs.
In August 2026, Bedrock-equipped excavators began doing fully autonomous, paid work on live commercial job sites. We've more than doubled our customer base since 2025, and our machines now work with contractors across the South, Midwest, and Mid-Atlantic
In October 2026, we raised a $600M Series C co-led by CapitalG and Emergence Capital, bringing our total funding to nearly $1B. We're using it to put more autonomous machines to work, build the field and operations teams behind every deployment, and expand into new machine types and job-site tasks.
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.
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.
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
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%
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!
You spend your day turning messy, cross-functional problems into structured insights and scalable fixes—so the product works reliably at scale.
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.
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)
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
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.
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
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)
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
Bedrock Robotics is an Equal Opportunity Employer
We’re committed to building a diverse and inclusive workplace. We consider all qualified applicants for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, ancestry, age, disability, veteran status, genetic information, or any other protected characteristic.
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.