Diligentrobotics

Fleet Engineer

1mo ago

USAroot cause analysisfmeabench testing

Lead troubleshooting and reliability improvement of deployed robotic fleet through data analysis and hardware failure investigation.

Fleet Reliability & Hands-On Debugging: Lead triage and bench/lab failure analysis across the mobile base, charging/docking, motion and power, connectivity, and sensor hardware — getting hands-on with returned units to reproduce, instrument, and isolate the failure.
Root Cause Analysis: Diagnose failures from component level (electrical, mechanical, firmware) to system level, applying standard methodologies (5-why, fishbone, fault-tree, FMEA, 8D).
Data-Driven Investigation: Pull and analyze fleet data and logs to define problem statements, surface trends, and validate hypotheses quantitatively — accounting for confounding factors, base rates, and sample size.
Field Synthesis & On-Call: Turn field-technician reports into crisp problem statements; own escalated issues (on-call) to support the field team and minimize downtime.
Corrective Action & Cross-Functional: Drive short- and long-term fixes (hardware, software, operational, process) to closure with engineering, operations, and product — including supplier corrective actions and design feedback with vendors and manufacturing. 
Tooling & Test Infrastructure: Build the fixtures, instrumentation, and bench test setups that accelerate debug workflows.
Documentation & Standards: Document debugging procedures and root-cause findings; contribute to fleet reliability standards.
Growth: Raise the team's investigative rigor, work closely with technicians, and grow into mentoring over time.

Hands-on electrical debugging — schematics, multimeter/oscilloscope, power, connector and harness fault isolation, basic instrumentation.
Hands-on mechanical debugging — mechanisms, tolerances and fits, fixturing, dimensional/force measurement, mechanical drawings; able to pinpoint what is physically wrong with a unit.
Hypothesis-driven, quantitative debugging — frame the problem, design discriminating tests, reason about confounding factors, base rates, and sample size, and update conclusions when the evidence contradicts them.
System log analysis — read raw system/robot logs to reconstruct events and isolate failures (the backbone of most investigations).
End-to-end versatility — comfortable across subsystems, running an investigation independently to conclusion.
3+ years in robotics, autonomous vehicles, or complex electro-mechanical systems — or an adjacent field (medical devices, industrial automation, semiconductor and capital-equipment field service, automotive or aerospace Maintenance/Repair/Overhaul, EV charging). 5+ years / senior scope preferred.
Bachelor's in Electrical, Mechanical, Mechatronics, Robotics, or a related engineering field (required); Master's a plus.

Experience with robotics stacks (ROS or equivalent) and robotic sensor calibration/test.
Experience with deployed robot or autonomous-vehicle fleets.
Networking (Ethernet, CAN bus, time-sync) and firmware familiarity.
Driving supplier corrective actions and design feedback with vendors and manufacturing.
Hardware-in-the-loop test and validation-rig design.
Compute platforms (NVIDIA Jetson/Orin, GPUs).

Diligent builds helpful robots that work safely and autonomously in real world environments. We move quickly, solve messy problems, and care deeply about reliability at scale. As a Fleet Engineer, you'll own the reliability and continuous improvement of our deployed robotic fleet — leading hands-on investigations into how and why robots fail in the field, across the mobile base, charging/docking, motion and power, connectivity (modem), and sensor hardware. You'll combine remote data analysis with bench/lab failure analysis at our Austin HQ, turning field-technician reports and fleet data into clear problem statements, validated root causes, and corrective actions driven to closure with engineering, operations, manufacturing, and vendors.
This role is based in Austin, TX. It will require 15-20% travel along with close collaboration across software, hardware, operations, and product engineering teams.
Improved FPY and reduced rework rates across production builds.
Reduced per-unit cycle time for test/provisioning while increasing test coverage.
Stable, fully automated provisioning flow with minimal manual intervention.