Senior Test Engineer - M​/F​/D

About Navflex

At Navflex, we're pioneering the future of logistics automation through cutting-edge AI and

robotics. Our autonomous mobile robots (AMRs) are transforming the way goods are loaded

and unloaded, enabling plug-and-play solutions that streamline operations and enhance

efficiency across the global supply chain for some of the world’s most demanding warehouse

Environments. Our international, cross‑disciplinary team in the EU and USA pairs robust mechatronics with cutting‑edge navigation and perception to deliver safe, reliable autonomy in real‑world warehouses and yards. Join us in shaping the future of intelligent logistics.

What makes us different

• We bring hardware and AI into production, not just prototypes

• We work directly with real warehouse operations and customer environments

• We build systems that must be safe, reliable, and economically viable

What you can expect at the Munich Team

You will join a highly skilled, hands-on engineering team working at the intersection of robotics, product development, and real-world deployment, with direct impact on systems used by global customers.

What We Offer

• The opportunity to work on robotics systems that are already used in real-world environments

• A role where you can actively shape systems and see the impact of your work

• A collaborative, international team with diverse backgrounds and perspectives

• Plenty of room for personal and professional growth as we continue to scale

• Challenging work alongside experienced engineers who are passionate about what they do

• A culture that values ownership, learning, and continuous improvement

• Competitive compensation

The Role

You will own system-level validation of Navflex’s autonomous forklift AMR—by continuously testing new software on real vehicles, stress-testing the system against the operational domain, and building automated test infrastructure that closes the gap between lab results and field performance.

This is not a “ticket-only QA” role. You will reproduce issues, analyze logs and data, and drive investigations far enough to pinpoint the most likely subsystem (perception, localization, planning, controls, HW interface, fleet services) and the fastest path to root cause and fix.

Success Measures - Success in this role will be measured by:

• Regressions are caught early and software is released with evidence-based readiness.

• Test coverage expands across the operational design domain (ODD), including edge cases and failure modes.

• Field issues become reproducible in lab/replay/HIL with clear fault isolation and actionable engineering feedback.

• A scalable automation framework exists (SIL/HIL + log replay + on-robot tests) with dashboards and automated reporting.

What You’ll Do

• Own scenario-based validation across the operational domain

  • Define and maintain an Operational Design Domain (ODD) and scenario library that reflects real warehouse and trailer-loading workflows (normal operation, edge cases, abuse cases).

  • Translate requirements and known risks into testable acceptance criteria, pass/fail metrics, and release gates.

  • Design and execute functional, regression, performance, reliability, and safety validation plans at the system level.

  • Plan long-duration stability/endurance campaigns and track reliability trends over time.

• Own day-to-day test execution on a vehicle fleet: smoke tests, nightly/weekly regressions, and targeted experiments for new features and bug fixes.

• Run structured test sessions that intentionally challenge autonomy behavior in a safe, controlled way (tight spaces, occlusions, pallet variability, trailer geometry, degraded sensors, etc.).

• Support pilots and customer transitions; ensure evidence-based sign-off for rollout and ramp.

Debug, reproduce, and isolate issues using logs and data

• Reproduce failures using on-robot tests, log replay, simulation, and HIL as appropriate.

• Analyze logs/traces and recorded datasets (e.g., ROS bag files, telemetry, event logs) to identify failure signatures and likely root causes.

• Create high-signal bug reports with minimal repro steps, expected vs. actual behavior, and supporting evidence (plots, log snippets, metrics).

• Partner with engineers to drive issues to closure and prevent…