Physical AI R&D Engineer

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.

Our objective is autonomy in unstructured, real-world environments: vehicles that build a rich spatial understanding of their surroundings, reason about how they'll change, and act decisively within them. Progress compounds: fleet data becomes the substrate for world modeling and simulation, which lets us validate new behaviors faster than physical testing alone.

You'll work on problems where evaluation methodology is still evolving, and you'll contribute to shaping it alongside senior team members. We expect you to track the literature, judge what is ready for real hardware, carry results from prototype to validated capability, and work with core software engineering to bring it into production behind clear requirements and acceptance criteria. Staying current is part of the job, not something you do on your own time.

This is research and development, not research alone. We work on open problems, but always against a product roadmap, and a result counts when it reaches a vehicle and changes something a customer experiences. We move quickly, and nobody here disappears into a lab.

Perception already runs on our vehicles across LiDAR, depth cameras, inertial sensing, and wheel odometry. You'll deepen its understanding: improve the accuracy and robustness of today's models, harden them against the conditions that degrade performance, and decide what should replace them.

The larger goal is a probabilistic 3D representation of the vehicle's surroundings that holds up over time: persisting through occlusion, decaying honestly when the world may have changed since it was last seen, and carrying calibrated uncertainty the rest of the stack can rely on. Warehouses are dynamic: pallets, people, and other vehicles move while unobserved. How it's encoded is an open question you'll help answer.

The same work extends into state estimation, where learned components complement the classical stack our autonomy team maintains: visual odometry, degeneracy prediction in geometrically weak environments, and pose uncertainty that's trustworthy rather than optimistic.

• Models you build run on production vehicles, with field behavior backed by evaluation developed in partnership with the team, with your own contributions clearly reflected in the results

• Capabilities you prototype reach production because you contribute clear, well-documented work that the team can build against

• You help evaluate external methods and contribute to team discussions on what's worth pursuing

• Take promising methods from the literature through prototype to validated on-vehicle capability, then partner with core software engineering to turn a proof of concept into production code

• Train, evaluate, and iterate on models using real fleet data: logs, rosbags, and multi-modal sensor streams from vehicles in production

• Build and maintain the 3D representation the rest of the stack consumes, with the calibration and sensor health monitoring it depends on

• Contribute to baselines and evaluation methodology, applying and refining standards set by the team

• Work…