Sensor Fusion and Localization Engineer

Zendar is looking for a Sensor Fusion & Localization Engineer to join our Lindau am Bodensee office. We are deploying a 360‑degree radar‑based perception system and extending it toward full‑stack autonomy through early fusion of camera and radar data. You will work on localization, mapping, calibration, and multi‑sensor fusion algorithms that allow autonomous systems to operate robustly across automotive and robotics domains.

This is a unique opportunity to join a team that is not bogged down by legacy code. You will define, own, and build a next‑generation perception stack that enables reliable autonomy at scale.

Zendar is building perception for physical AI—giving engineers a strong foundation for creating world‑class robotics applications. At Zendar, you’ll work on perception foundation models that enable robots to understand and interact with their environments across a wide range of industries.

Zendar pioneered RF perception that delivers a vision‑like, semantically segmented understanding of the environment—running on embedded automotive systems using only radar data. This RF perception forms the backbone of Zendar’s next‑generation foundation models, which are built around early fusion of RF and vision data.

This architecture inverts the traditional perception stack. Instead of treating RF signals as secondary, Zendar’s models combine vision’s high angular resolution with RF’s strong temporal and spatial understanding at the earliest stages of perception. The result is a system that sees farther, remains robust to occlusion and adverse weather, and operates far more efficiently than vision‑only or lidar‑based approaches.

See a demo of Zendar’s foundational RF perception.

At Zendar, you’ll work at the cutting edge of autonomous mobility and robotics—advancing foundation models that will power the next generation of physical AI systems. You’ll work with large‑scale, real‑world, multi‑modal datasets composed of synchronized and calibrated radar, camera, and lidar data collected across multiple continents.

Our team brings together deep expertise across hardware, signal processing, machine learning, and software engineering, with decades of experience in sensing and perception. We are a global team with offices in Berkeley, Lindau (Germany), and Paris (France). Zendar is well‑funded by leading Tier‑1 venture capital firms and has established strong industry partnerships.

Although AI is central to what we build, our hiring process is intentionally human: every resume is reviewed by a real person.

As a Sensor Fusion & Localization Engineer in Lindau, you will design and implement the state estimation framework behind Zendar’s next‑generation autonomy stack. You will work across radar, vision, and inertial sensing to develop precise, robust, and efficient algorithms for localization, odometry, mapping, and calibration.

You will have direct access to raw spectral radar data and the opportunity to treat radar as a primary sensor in robotics state estimation.

• Ownership: You will drive architectural decisions, making rigorous tradeoffs between approach A vs.
  
  B.
• Scale: You will work with a real‑world dataset covering tens of thousands of kilometers across multiple continents.
• Impact: You will see your work validated on real vehicles, bridging the gap between research and production.

• Architect a Multi‑Sensor State Estimation System
  • Own the design and implementation of a multi‑sensor robotics and state estimation framework.
  • Develop tightly coupled fusion architectures for streaming camera, radar, and IMU data, with an emphasis on real‑time performance, robustness, and production scalability.
• Deliver Production‑Ready Algorithms
  • Multi‑sensor SLAM and localization
  • High‑precision online calibration
  • Radar‑camera‑IMU sensor fusion
  • Navigation for autonomous vehicles
• Drive Reliability
  • Target "four nines" reliability behavior in defined conditions, focusing on the messy long tail of real‑world driving.
• Optimize for Real‑Time
  • Partner with embedded teams to ensure models meet strict constraints (latency, memory, throughput) and integrate cleanly via stable interfaces.