Staff Systems Engineer

About Us

Are you ready to build the future of supply chain? At Gather AI, we’re not just creating software; we’re pioneering a new era of warehouse intelligence. We’ve developed a groundbreaking, vision-powered platform that uses autonomous drones and existing equipment to capture real-time data, completely digitizing workflows that have historically been manual and error-prone. This means facilities operate smarter, safer, and more efficiently, ultimately redefining “on-time, in full” delivery.

If you’re looking for an opportunity to contribute to truly transformative technology and make a significant impact in a vital industry, Gather AI is the place for you. We’re leading the charge in the rapidly evolving robotics industry, and we invite you to join us in reshaping the global supply chain, one intelligent warehouse at a time.

About the Team

You'll be joining a small, senior autonomy team working across two production platforms — drone-based inventory scanning and MHE Vision — deployed in real customer warehouses today. The team spans autonomy, perception, ML, and cloud infrastructure, and operates with a high degree of ownership and minimal hierarchy. You'll collaborate closely with engineers across the full stack as well as product management and customer-facing teams to shape how these platforms scale. This is a fast-moving environment where your technical decisions have direct, visible impact on the product and the customers who depend on it.

About the Role

We are looking for two Staff Systems Engineers to take technical ownership of one of our product platforms — Drone or MHE Vision — and bring systems-level rigor to how we build, deploy, and operate it at scale. Reporting to the Director of Autonomy, Brad Hamner, you will own the full vertical from hardware integration through cloud delivery, improving reliability, integration test coverage, deployment velocity, and cross-subsystem observability.

As a Staff Systems Engineer, you'll drive technical direction across teams, own outcomes end-to-end, and operate with direct visibility to product and company leadership.

What You’ll Do

• Take full technical ownership of an assigned product platform (Drone or MHE Vision) — understanding the system end-to-end and driving its reliability, scalability, and operational maturity


• Streamline the deployment pipeline to reduce friction and improve repeatability, including CI/CD for embedded and containerized components and fleet-wide update mechanisms


• Lead cross-subsystem integration hardening: edge-to-cloud data flow, model deployment pipelines, sensor-to-perception orchestration, and production monitoring and observability


• Establish and track product performance metrics (scanning accuracy, uptime, deployment velocity) and drive improvements across engineering teams


• Collaborate with Product Management on roadmapping, requirements translation, and customer deployment planning; conduct site visits to understand the product in its operating environment


• Drive technical alignment across the autonomy, ML, and cloud engineering teams

What You’ll Need

• 10+ years of engineering experience shipping production systems, with significant breadth across embedded/edge compute, cloud infrastructure, and data pipelines


• Systems architecture experience — must have designed and owned multi-subsystem platforms spanning hardware, edge compute, cloud, and data layers


• Cross-team technical leadership — demonstrated ability to drive alignment and delivery across 2+ engineering teams without direct authority


• Production deployment at scale — must have shipped and operated real systems with real reliability requirements, not just prototypes


• Python proficiency, Linux fluency, CI/CD tooling, Docker, and C++ familiarity; experience with embedded/edge platforms (Jetson, ARM)

Nice to Have

• Robotics or drone systems experience (ROS, flight controllers, sensor integration, autonomy stacks)


• Computer vision or ML pipeline integration (model serving, inference optimization, data labeling pipelines)


• Cloud infrastructure and backend systems (AWS/GCP, infrastructure-as-code, scalable data pipelines)


• Warehouse or logistics domain experience (operational constraints, customer deployment patterns)