Director of Quality

About the role

Own and evolve QA strategy across all three product surfaces — drone hardware and flight systems, MHE Vision perception, and the cloud/fullstack customer platform — including release qualification gates that span the full stack.

Build automated test infrastructure from a low baseline: CI/CD test gates, API and UI regression, data pipeline validation, hardware-in-the-loop, and simulation for physical systems.

Stand up an AI-assisted QA pipeline — LLM-driven test generation, automated failure triage, model output grading, and AI-driven exploratory testing — to scale QA throughput alongside an AI-accelerated engineering org.

Lead, mentor, and level up the distributed QA team across the US and India, including directly developing the existing QA Manager and growing the team with a deliberate mix of automation engineers and high-judgment manual testers.

Establish ML/perception model evaluation methodology and release qualification criteria, working directly with ML and autonomy leads to qualify model upgrades for production.

Partner cross-functionally with engineering, autonomy, cloud, and field ops to align QA with deployment realities and customer-reported quality issues.

Requirements

• 10+ years in QA or test engineering with production systems at scale, including 5+ years leading QA, SDET, or test engineering teams
• Demonstrated breadth across at least two of: hardware/embedded systems QA, ML/computer vision model evaluation, and cloud/fullstack/SaaS platform QA — single-domain specialists will not have the range this role requires
• Proven track record of replacing manual test cycles with automation, instrumentation, or AI grading — and a clear philosophy for where manual QA remains irreplaceable
• Experience managing a geographically distributed team (US + India or equivalent), with the mentorship instincts and cultural fluency that requires
• Hands-on technical depth: Python, CI/CD (GitHub Actions, Jenkins, or equivalent), pytest or equivalent test frameworks, Docker, AWS or GCP, and familiarity with at least one of hardware-in-the-loop, robotics simulation, or ML model evaluation harnesses
• Pittsburgh-based or genuinely willing to relocate — this is an on-site/hybrid role and is not eligible for full remote