Field AI is transforming how robots interact with the real world. We are building risk-aware, reliable, and field-ready AI systems that address the most complex challenges in robotics, unlocking the full potential of embodied intelligence. We go beyond typical data-driven approaches or pure transformer-based architectures, and are charting a new course, with already-globally-deployed solutions delivering real-world results and rapidly improving models through real-field applications.
We're looking for an Engineering Lead, Effectiveness & Delivery to design and implement the engineering systems, tooling, and workflows that make our organization faster, more reliable, and more connected — from how we identify and resolve bugs, to how we plan and deliver software, to how work flows across product, engineering, QA, and field teams. This is a high-leverage role where you will architect solutions that directly improve how every engineer at FieldAI ships software.
This is a hands-on engineering role, not a coordination role. You will diagnose systemic problems, design solutions, build and ship them, and measure the results. You'll need deep engineering judgment, a strong sense for developer experience, and the ability to earn trust across teams through the quality of your work.
Architect and implement engineering workflow improvements that measurably reduce bug cycle time — from issue identification through triage, assignment, and resolution
Redesign and consolidate our Jira project architecture into a unified workspace that eliminates fragmentation, improves cross-team visibility, and reduces overhead for engineers
Design and build triage and routing systems, automation, and integrations that remove manual friction from how issues move through the organization
Establish engineering-led delivery practices — work decomposition standards, estimation frameworks, and definition-of-done criteria — that make project execution more predictable without adding unnecessary process
Design lightweight systems that give engineering leadership accurate, real-time visibility into progress, risk, and capacity
Identify and remove structural bottlenecks in how work flows from planning through delivery, diagnosing root causes rather than treating symptoms
Build and implement end-to-end toolchain integrations that create traceability from product intent through engineering work, QA validation, and field outcomes
Define engineering-owned interfaces and contracts between functions — establishing clear, enforceable definitions of what "ready for engineering," "ready for QA," and "ready for field" actually mean
Evaluate, select, and integrate tooling that gives each function appropriate visibility without requiring manual synchronization or status reporting