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Test Fleet Engineer Jobs (NOW HIRING)

As a Fleet Engineer, you'll own the reliability and continuous improvement of our deployed robotic ... Tooling & Test Infrastructure: Build the fixtures, instrumentation, and bench test setups that ...

As a Fleet Engineer, you'll own the reliability and continuous improvement of our deployed robotic ... Tooling & Test Infrastructure: Build the fixtures, instrumentation, and bench test setups that ...

As a Fleet Engineer, you'll own the reliability and continuous improvement of our deployed robotic ... Tooling & Test Infrastructure: Build the fixtures, instrumentation, and bench test setups that ...

Technician II

Portland, OR ยท On-site

$25.50 - $34.25/hr

... vehicle test fleet vehicle registration Maintain asset registration Support logistics team as needed for parts tracking, and parts coordination Create & maintain log of Engineering change requests ...

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How much do test fleet engineer jobs pay per hour?

As of Sep 10, 2026, the average hourly pay for test fleet engineer in the United States is $46.58, according to ZipRecruiter salary data. Most workers in this role earn between $34.86 and $59.13 per hour, depending on experience, location, and employer.

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Infographic showing various Test Fleet Engineer job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 86% Full Time, 10% Part Time, 2% Contract, and 1% Nights. Highlights an 92% Physical, 1% Hybrid, and 7% Remote job distribution, with an average salary of $96,884 per year, or $46.6 per hour.

Fleet Engineer

Austin, TX โ€ข On-site

Diligent Robotics
Health Care and Social Assistanceย โ€ขย 51 - 200 employees

Full-time

Re-posted 6 days ago


Job description

What we're doing isn't easy, but nothing worth doing ever is.ย 

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.

Key Responsibilities

  • 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,ย 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 - includingย supplier corrective actions and design feedback with vendors and manufacturing.ย 
  • 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.

What Success Looks Like

  • 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.

Basic Qualifications

  • 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.

Preferred Qualifications

  • 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).