Zendar is looking for a Machine Learning Research Engineer to join our Paris office. We are currently deploying one of the world's most advanced 360-degree radar-based perception systems. We are now expanding our capabilities to deliver full-scene perception using the early fusion of camera and radar, scaling these technologies across the automotive and robotics industries.

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.

About Zendar:

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 résumé is reviewed by a real person.

Your Role:

Zendar’s "Semantic Spectrum" technology extracts rich scene understanding from radar sensing. As a ML Research Engineer in Paris, your goal is to evolve this technology into a multi-modal foundation model architecture.

You will design and implement the architecture end-to-end. This involves training models from scratch on massive datasets, defining evaluation metrics for long-tail validation, and partnering with platform teams to ensure successful deployment in real-time embedded systems.

Why this role is exciting:

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.

What You’ll Do:

Architect Multi-Sensor Strategies: Own the technical strategy for multi-sensor perception models. Design fusion architectures for streaming inputs (camera/radar/Lidar) utilizing early fusion and temporal fusion.
Deliver Production-Ready Models: Build and deploy models for:
- Full-Scene Understanding: Occupancy grids, free-space, and dynamic occupancy.
- 3D Perception: Object detection and tracking.
- Static Environment: Lane line and road structure estimation.
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.

What We Look For:

Experience: 5+ years of experience (or a PhD) designing and implementing ML systems, with demonstrated ownership of research-to-production outcomes.
Deep Learning Expertise: Strong background in perception, specifically transformer-based architectures, temporal modeling, and multi-modal learning.
Training Mastery: Demonstrated experience training large models from scratch (not just fine-tuning) on large-scale datasets.
Engineering Proficiency: Proficient in Python and a major deep learning framework (PyTorch or TensorFlow).
Strategic Thinking: Ability to lead architectural discussions, articulate tradeoffs, quantify risks, and set realistic milestones.

Bonus Points:

Sensor Knowledge: Experience with multi-sensor fusion (camera, radar, Lidar) and the nuances of real-world sensor noise.
Advanced Education: PhD in Machine Learning, Computer Vision, or Robotics.
Foundation Models: Experience with multi-modal pretraining, self-supervised learning, and scaling laws/strategies for autonomy.
Modern Architectures: Familiarity with "Transfusion-style" paradigms (transformer-based fusion across modalities and time) and BEV-centric perception.
Advanced Perception Tasks: Experience with 3D detection, occupancy networks, tracking, and streaming inference.

What We Offer:

Opportunity to make an impact at a young, venture-backed company in an emerging market
Competitive salary ranging from €75,000 to €95,000 annually depending on experience and equity
Hybrid work model: in office 3 days per week (Monday, Tuesday, Thursday), the rest… work from wherever!
Modern Workspace: Fully equipped, modern office in the heart of Paris
Transportation/Commute: Commuter benefits (e.g., partial reimbursement for public transport or cycling programs, where applicable)
Subsidized meal vouchers (tickets restaurant)
Wellness Pass (ex Gymlib)

Zendar is committed to creating a diverse environment where talented people come to do their best work. We are proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status.