Dyna Robotics · San Francisco
OtherRobotics FullTimeposted 5mo ago
Dyna Robotics builds general-purpose robots powered by a proprietary embodied AI foundation model with top-in-industry generalization and real-world performance. Already deployed with customers across multiple industries, our robots do commercial-grade work in the physical world. Our team comes from Google DeepMind, Meta, and Cruise, and we're backed by CRV, First Round, and other leading investors.
Build and lead the QA function at Dyna across our full stack — cloud inference servers to Linux-based IoT devices on the factory floor. Real-world robotics cannot be fully validated in simulation; physical hardware behavior and real-environment variability require hands-on QA judgment. You will hire and mentor a QA team, define testing standards, and stay hands-on with critical validation — balancing automation with the physical testing our domain demands. We expect aggressive use of AI tools (LLM code generation, AI test assistants, GenAI test design) to multiply team output.
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 (inference server, station-side deployment). Run A/B testing for robotic tasks to measure success rates and latency. 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 (task success rate, cycle time, 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 — 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 (SOPs) for hardware and software validation; drive continuous process improvement.
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/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 — not expected to develop features.
Systems: Strong Linux administration skills and experience with IoT/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; simulation (Gazebo, MuJoCo), and CI/CD tools (Docker, Kubernetes).
AI Tools: Track record of adopting AI/LLM tools to accelerate QA workflows (test generation, bug triage, automation scaffolding). Must actively seek out and integrate new AI tools.
Hardware QA: Hands-on experience with physical robot testing, hardware bring-up, or manufacturing QA — understanding failure modes that only appear on real hardware.
Soft Skills: Strong leadership and people management; effective communicator across technical and non-technical stakeholders.
Experience with computer vision metrics (image quality, depth validation, camera calibration).
Experience with ML model evaluation, defining performance metrics for AI systems, or building automated regression pipelines for ML models.
Previous experience in a fast-paced startup environment.
At Dyna Robotics, we build technology for the real world, which requires a team as diverse as the environments our robots inhabit. We are an equal opportunity employer committed to technical rigor and mutual respect.
Don’t let a checklist stop you. Data shows that underrepresented groups often only apply if they meet 100% of the criteria. We value problem-solving and grit over keyword matching. If you’re passionate about robotics, no matter your discipline, we want to hear from you, even if you don't check every box.