Field Optimization

ABOUT THE ROLE AIM builds autonomous machines that must continuously improve as they operate in the real world. Performance is not theoretical - it’s measured in productivity, efficiency, and predictability on real jobsites. Field Optimization Engineers focus on making autonomy better at real tasks, closing the loop between field behavior and AI, planning, and control systems. About you You are analytical, curious, and deeply interested in how autonomous systems behave under real conditions. You enjoy working with operators, data, and models to improve performance over time. You are equally comfortable: - analyzing autonomy performance metrics and error distributions - observing operators and understanding task-level workflows - diagnosing why a behavior works in theory but fails in practice - translating field observations into model and planning improvements - validating changes through real-world testing You believe the field is the ultimate teacher - and that learning from it is a competitive advantage. About us together Together, we will: - improve task performance across dozing, excavation, loading, and more - identify the signals that make autonomy more robust and adaptive - validate learning and model improvements in the real world - feed high-quality insights back into AI and planning teams - turn field variability into system strength We’ll iterate relentlessly, test under stress, and make autonomy measurably better with every deployment. RESPONSIBILITIES Task Optimization & Performance - Tune autonomy behaviors for specific tasks and environments. - Analyze performance metrics, failure modes, and edge cases. - Validate ConOps, safety assumptions, and degraded-mode behavior against real site conditions. Signal Discovery & AI Feedback - Identify patterns in autonomy behavior and failure. - Translate field insights into actionable inputs for AI and planning teams. - Validate improvements under real operating conditions. Testing & Validation - Design and execute on-site test plans (functional, performance, environmental, regression) for autonomy behaviors, safety systems, control responses, and telemetry reliability. - Validate releases, patches, hardware revisions, and autonomy updates under real operational stress. - Maintain clear bug reports, defect triage, downtime logs, and structured feedback cycles into engineering. - Drive quality upstream - flagging architectural/testability gaps early and championing improvements. Product Integration & Custom Engineering - Write production-grade code when needed - scripts, integrations, custom workflows, or quick fixes to unblock deployments. - Build customer workflow integrations (data APIs, cloud ingestion, operator UI tweaks, etc.). - Partner with autonomy, software, and hardware teams to validate features in the field and influence product direction. Travel - Deep immersion on customer sites worldwide (US, Australia, Africa, Europe). - ~40-70% travel depending on deployment cycles. QUALIFICATIONS Basic qualifications - Bachelor’s degree in engineering (Mechanical, Electrical, Computer, Robotics, Systems) or equivalent practical experience. - 5+ years experience in one or more: field engineering, systems engineering, SDE/SDE-T, robotics deployment, industrial automation, test/validation engineering. - Hands-on experience debugging complex hardware/software systems under real-world constraints. - Strong scripting ability (Python, Bash, JavaScript, or equivalent) and comfort writing production-quality scripts or small features. - Strong understanding of sensors, networking, Linux systems, compute modules, and embedded hardware fundamentals. - Experience designing/executing test plans and reporting structured quality findings. - Excellent communication skills - able to translate field issues into crisp engineering requirements. - Ability to work autonomously under ambiguous, high-pressure conditions. - Willingness to travel internationally and domestically, often on short not