About the Role:

This is an applied engineering role focused on developing real-world perception capabilities for complex multimodal sensing systems. The position is broad and versatile, combining computer vision, deep learning, sensor fusion, mapping, and signal-based analysis. In our startup environment, you will be involved throughout the development cycle – from working with raw sensor data and developing algorithms to validation, integration, and field testing – balancing advanced techniques with practical engineering solutions. This is a hybrid position in our Rehovot office.

What You’ll Be Doing:

Perception & Algorithm Development: Develop, implement, and improve computer vision, deep learning, and perception algorithms for real-world sensing systems.

Multimodal Perception & Sensor Fusion: Work with RGB, IR, radar, and other sensing modalities. Process, align, analyze, and combine information from multiple sensors to improve detection, tracking, and scene understanding.

Algorithm Implementation & Integration: Implement and integrate algorithms into existing systems, focusing on reliability, performance, and practical deployment constraints.

Data & Validation: Work hands-on with datasets, including preprocessing, labeling requirements, quality checks, benchmarking, edge-case analysis, and model performance evaluation.

Tooling & Automation: Develop scripts and pipelines for data processing, model training, evaluation, visualization, and experiment tracking.

Lab & Field Work: Participate in sensor calibration, data collection, field experiments, debugging, and system-level performance analysis.

Requirements:

• B.Sc. in Electrical Engineering, Computer Science, or a related field; M.Sc. is an advantage.
• 3+ years of hands-on experience in computer vision, deep learning, perception, sensor fusion, robotics, or related domains.
• Strong Python skills and experience developing reliable, maintainable code.
• Experience with PyTorch, TensorFlow, OpenCV, NumPy, Scikit-learn, or similar tools.
• Practical experience developing, training, and evaluating computer vision models or perception algorithms.
• Solid understanding of computer vision fundamentals, including detection, segmentation, tracking, camera geometry, calibration, or 3D perception.
• Hands-on experience with at least one additional sensing modality beyond conventional computer vision, such as radar, sonar, LiDAR, IR, depth, inertial sensors, or other time-series sensor data.
• Experience with sensor fusion, multimodal perception, mapping, localization, or signal processing is a strong advantage.
• Experience with image anomaly detection is an advantage.
• Experience with Linux, Git, and collaborative development workflows.
• Experience developing data-processing, evaluation, or automation pipelines is an advantage.
• Experience with CUDA, TensorRT, runtime optimization, or embedded deployment is an advantage.

Skills:

• Strong applied engineering mindset, with the ability to translate technical challenges into practical, testable solutions.
• Comfortable working across computer vision, multimodal sensing, data analysis, and system-level debugging.
• Hands-on and adaptable, enjoying the variety and broad ownership of a startup environment.
• Pragmatic and delivery-oriented, balancing state-of-the-art approaches with practical solutions when needed.
• Independent, proactive, and accountable, with a structured approach to problem-solving and validation.
• Strong communication skills and ability to collaborate across algorithms, software, hardware, and system teams.
• Team player with attention to detail and commitment to high-quality engineering.