QA Lead, Robotics

About the role

You will own the end to end quality assurance function for a robotics company operating at the edge and in production environments. This role requires you to translate ambiguous real world conditions into rigorous testing strategies that keep pace with rapid iteration. You will balance deep hands on validation with scalable automation to ensure shipped systems meet high reliability bars. A core part of the role is mentoring engineers and driving a culture where intellectual honesty and psychological safety are non negotiable. You will leverage modern AI tooling to extend the reach of the team without sacrificing rigor. The position demands comfort with both cloud native software and the constraints of embedded Linux on physical devices. You will be responsible for guarding the release process from regressions that could impact customers in the field. This role sits at the intersection of robotics, machine learning, and infrastructure quality.

Key facts

What you'll do

• People Management: Hire, develop, and retain a QA team. Provide career development and feedback, delegate ownership to grow engineers, and monitor team energy to prevent burnout. Drive recruiting and foster a culture of intellectual honesty and psychological safety.
• QA Strategy & Cross-Functional Alignment: Own the QA strategy and roadmap. Align priorities with company vision, translate quality risks into business impact, and drive alignment across robotics, ML, and infrastructure teams. Shield the team from organizational noise.
• Test Automation & CI/CD (primary focus): Build and maintain scalable automated test frameworks across robot software, cloud, and edge devices. Partner with developers to continuously expand CI coverage and shift testing left, minimizing manual testing over time.
• Release Validation & Benchmarking: Own and automate release checklists for inference server and station-side deployment. Run A/B testing for robotic tasks to measure success rates and latency. Perform manual validation where automation is not yet feasible.
• AI Model Evaluation & Regression Testing: Design and execute evaluation pipelines that validate AI model and full-system performance across model, software, and hardware updates. Define and track key metrics such as task success rate, cycle time, and generalization across environments. Build automated regression benchmarks and gate releases on evaluation results to prevent regressions from reaching production.
• System Monitoring & Alerting: Use tools like Datadog and Grafana to track system health, identify performance regressions, and maintain observability across production environments.
• Physical Hardware QA & Integration: Validate real robot behavior that simulation cannot cover, including mechanical repeatability, sensor calibration drift, and edge cases on physical hardware. Troubleshoot integration issues across firmware, drivers, and application software.
• Process & Documentation: Establish and maintain standard operating procedures for hardware and software validation. Drive continuous process improvement across testing workflows.
• Tooling and Infrastructure for Testing: Contribute to the design of testing infrastructure that supports both cloud inference and edge compute constraints. Ensure test environments reflect production conditions as closely as possible.
• Collaboration with Product and Engineering: Work closely with product managers and engineers to define quality gates, acceptance criteria, and field failure analysis processes. Translate customer reported issues into reproducible test cases.
• Adoption of AI Assisted QA: Drive aggressive use of AI tools such as LLM code generation, AI test assistants, and GenAI test design to multiply team output. Encourage experimentation and measured adoption of new AI techniques.
• Metrics and Reporting: Define, collect, and report on quality metrics that inform leadership decisions. Use data to prioritize testing efforts where risk is highest.

Requirements

• Education: Bachelor's or Master's in Computer Science, Robotics, or a related field.
• Experience: 7+ years in QA, SRE, or Robotics Engineering with hands-on experience testing real hardware systems (not purely software or simulation), including 2+ years in a team lead or management role.
• Technical Skills: Proficiency in Python. Comfortable navigating build systems, running test pipelines, and debugging failures across the stack without needing to develop features.
• Systems: Strong Linux administration skills and experience with IoT or edge device deployments.
• Testing Tools: Deep experience in developing and managing automated testing frameworks. Familiarity with modern AI/ML testing, model evaluation, and regression benchmarking techniques. Exposure to simulation tools such as Gazebo or MuJoCo, and CI/CD tools including Docker and Kubernetes.
• AI Tools: Track record of adopting AI and LLM tools to accelerate QA workflows, including test generation, bug triage, and automation scaffolding. Must actively seek out and integrate new AI tools to improve efficiency.
• Hardware QA: Hands on experience with physical robot testing, hardware bring up, or manufacturing QA. Understanding of failure modes that only appear on real hardware and not in simulation.
• Soft Skills: Strong leadership and people management capabilities. Effective communication across technical and non technical stakeholders.

Nice to have

• Experience with computer vision metrics such as image quality assessment, depth validation, and camera calibration.
• Experience with ML model evaluation, defining performance metrics for AI systems, or building automated regression pipelines for ML models.
• Previous experience working in a fast paced startup environment.

Practical notes

This is a full time position based in Redwood City, California. The role may require limited travel within the San Francisco Bay Area for hardware testing or cross team alignment. Candidates must be eligible to work in the United States without sponsorship at this time.