QA

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.

The Role

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.

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 (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.