About the TeamQuality at Gather AI spans three genuinely distinct surfaces: drones flying in warehouses, camera-based MHE Vision systems installed on forklifts and at dock doors, and a customer-facing cloud platform that ingests it all and turns it into inventory intelligence. Our QA team - currently anchored by a QA Manager and engineering team primarily based in India - has been doing strong work. But as our product lines scale and our engineering org accelerates with AI tooling, we need a Director who can raise the entire function to match.
About the RoleMost QA Director roles hand you a legacy function and ask you to keep the lights on. This one hands you a genuine mandate:
build the QA strategy for an AI-first robotics company from the ground up - across hardware, ML models, and cloud software simultaneously.
As our Director of Quality Assurance, you will own the full QA surface: automated test infrastructure for drone flight and docking systems, perception model regression and evaluation, and cloud/API/UI regression for the customer platform. You'll lead a distributed US and India team, establish release qualification gates that span hardware-to-cloud, and build an AI-assisted QA pipeline that scales throughput as the engineering org's velocity grows. This is a role with executive visibility, real authority, and the rare opportunity to define what "quality" means at a company where the product is this technically diverse.
What You'll Do- 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
What You'll Need- 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
Nice to Have- Hardware QA or field reliability experience with sensors, cameras, embedded devices, drones, or robotics
- Experience with simulation-based testing (Gazebo, Isaac Sim, or in-house sim) for robotics or perception systems
- Background in warehouse, logistics, or industrial deployment environments
- Experience building or operating QA for a multi-tenant SaaS platform at scale
