Director of Quality

Director of Quality Assurance

About Us

At Gather AI, we are pioneering a new era of warehouse intelligence. Our groundbreaking, vision-powered platform uses autonomous drones and existing equipment to capture real-time data, completely digitizing historically manual and error-prone workflows. This enables facilities to operate smarter, safer, and more efficiently, redefining "on-time, in full" delivery. We are leading the charge in the rapidly evolving robotics industry and reshaping the global supply chain.

About the Team

Quality at Gather AI spans three distinct surfaces: drones flying in warehouses, camera-based MHE Vision systems on forklifts and at dock doors, and a customer-facing cloud platform that ingests data to provide inventory intelligence. Our QA team, currently anchored by a QA Manager and an engineering team in India, has been doing strong work. As our product lines scale and our engineering organization accelerates with AI tooling, we need a Director to elevate the entire QA function.

About the Role

This role offers a genuine mandate: build the QA strategy for an AI-first robotics company from the ground up, encompassing hardware, ML models, and cloud software simultaneously. As the Director of Quality Assurance, you will own the full QA surface, including 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 spanning hardware-to-cloud, and build an AI-assisted QA pipeline to scale throughput alongside the engineering organization's growing velocity. This is a role with executive visibility, significant authority, and the rare opportunity to define "quality" at a company with such technically diverse products.

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, including 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, incorporating LLM-driven test generation, automated failure triage, model output grading, and AI-driven exploratory testing to scale QA throughput.
• Lead, mentor, and level up the distributed QA team across the US and India, directly developing the existing QA Manager and growing the team with a 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.
• Proven track record of replacing manual test cycles with automation, instrumentation, or AI grading, with a clear philosophy for where manual QA remains irreplaceable.
• Experience managing a geographically distributed team (US + India or equivalent), with mentorship instincts and cultural fluency.
• Hands-on technical depth: Python, CI/CD (GitHub Actions, Jenkins, or equivalent), pytest or equivalent test frameworks, Docker, AWS or GCP, and familiarity with hardware-in-the-loop, robotics simulation, or ML model evaluation harnesses.
• Must be Pittsburgh-based or genuinely willing to relocate; this is an on-site/hybrid role and 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.