Staff Full Stack Engineer

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.

You'll join the fullstack and WMS integration teams at a pivotal moment: live systems, real enterprise customers, and a platform being deliberately re-engineered from growth-era tradeoffs into a durable, enterprise-grade foundation. The team owns everything from customer-facing integrations to the core services and data layer beneath them, and works in close partnership with product, security, and customer success to turn ambiguous requirements into dependable, well-architected systems.

Most staff engineering roles ask you to choose between architecture and code, or between engineering and customers. This one doesn't.

As Staff FullStack Engineer, you'll set the technical direction for how Gather AI's platform integrates with the world, while staying hands-on with the code, infrastructure, and Kubernetes clusters that run it. You'll lead the shift from ad hoc, growth-era integration patterns to enterprise-grade standards, elevate a live customer-facing solution alongside the engineer already driving it, and become the single US-based point of contact who turns customer requirements into shipped, scoped engineering work.

• →Architect and build across distributed systems, APIs, integrations, and infra shipping your own code with performance, fault-tolerance, and horizontal scale as first-class design concerns.
• →Own the platform's non-functional bar by setting explicit targets for scalability, reliability, availability, latency, performance, security, and observability, and holding the full stack to them.
• →Partner with team members on multiple projects like MHEV, Drone and 3DCC to elevate and deliver solutions in technical design and take new requirements to production at high SDLC standards (testable, observable, well-scaled, tuned DB access).
• →Decompose overloaded APIs into well-bounded services and introduce data-access abstractions that control DB connections and improve scalability.
• →Retire reliability and compliance debt (e.g., PostgreSQL upgrades), stand up production observability metrics and alerts, and reduce delivery friction with CI/CD and AI-assisted delivery.
• →Serve as the US-based single point of contact for fullstack work, partnering with Customer Success to turn customer requirements into clear, well-scoped engineering.
• →Mentor senior engineers and raise the bar on engineering practices across backend and infrastructure.

• 8+ years building and operating production software, with a track record of taking large, stateful systems to enterprise-grade scale and reliability
• Deep hands-on experience with Python, Go and Node.js, PostgreSQL at production scale, and Kubernetes on Azure, from architecture through independent production debugging
• Depth in APIs and integrations across internal service-to-service and external partner integrations: REST, protobuf, messaging, and OAuth, plus secure integration protocols across HTTP, SFTP, email, and IPSec, designed for resilience and secure data exchange
• Deep grasp of non-functional design: scalability, availability and failure modes, latency budgets, caching, and security as first-class concerns, not afterthoughts
• Solid, hands-on CI/CD: building and owning automated build, test, and deployment pipelines that make delivery fast and safe
• Production-grade observability: instrumenting logs, metrics, and traces, and using them (with SLIs/SLOs and incident response) to keep systems healthy
• Practical AI-assisted engineering, using tools such as Claude across the SDLC with sensible guardrails
• Establishing best practices that raise team's engineering standard
• Comfortable running customer-facing technical conversations and translating ambiguous requirements into scoped engineering work

• Domain experience in logistics, warehouse management systems, or robotics-adjacent platforms
• Experience taking systems multi-region with read-write replication or geo-distribution
• Experience adopting AI across the software development lifecycle, including GenAI code-validation guardrails