Position Overview

We’re looking for an experienced, hands-on Senior Perception Algorithm Engineer to lead the development of advanced perception capabilities for real-world, safety-critical environments.

This is a hands-on and delivery-oriented position that sits at the core of NIART’s perception stack, combining data from multiple sensing modalities, including cameras, Radar, LiDAR, geo-spatial data, GIS layers and more.

The position is a full-time, hybrid role based primarily in Rehovot, Park HaMada, conveniently located next to the train station.

YOU ARE: thriving when taking full, end-to-end ownership and seeing your algorithms deployed and working in the real world; capable to both design the algorithmic approach and also implement it, test it on field data and validate performance, while balancing state-of-the-art methods with practical solutions where needed.

Responsibilities

·       Perception & Algorithm Development: Design, develop, and optimize algorithms for real-world perception systems, including detection, tracking, scene understanding, geometry-based methods, and learning-based approaches for sensor fusion, radar-signals analysis, computer vision, classification, mapping, and anomaly detection.

·       Multimodal Sensor Fusion: Work with diverse sensing modalities, including RGB, IR, radar, and additional sensors or signal sources. Analyze, preprocess, align, validate, and fuse data from multiple sources to improve system robustness and performance.

·       End-to-End Ownership: Own algorithmic components from problem definition and data analysis through implementation, validation, integration, and performance monitoring.

·       Data & Validation: Define labeling requirements, review datasets, perform quality checks, mine edge cases, build benchmarks, and analyze model, data, and system failure modes.

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

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

Qualifications

·       B.Sc. in Electrical Engineering, Computer Science, Computer Engineering, or a related field; M.Sc. is an advantage.

·       6+ years of hands-on experience in perception, sensor fusion, multi-object tracking, signal processing, computer vision, deep learning, robotics, autonomous systems, or related domains.

·       Strong Python proficiency and experience writing production-quality code using PyTorch/TensorFlow, OpenCV, NumPy, and Scikit-learn, or similar tools.

·       Proven experience developing, validating, and deploying algorithms or neural-network models for real-world sensing or perception applications, including detection, segmentation, tracking, sensor geometry, calibration, 3D perception, or image-to-world transformations.

·       Hands-on experience beyond standard computer vision is required, with at least one additional sensing modality or signal source such as radar, sonar, LiDAR, time-series sensor data, or other sensor-based signals.

·       Experience with sensor fusion, multimodal perception, mapping or localization is a strong advantage.

·       Experience with radar perception, radar signal processing, or radar-based classification - advantage

·       Experience with GIS tools, map layers, spatial databases, or geo-data pipelines -advantage.

·       Experience with image anomaly detection is an advantage.

·       Experience working on autonomous systems, robotics, defence, mobility, smart infrastructure, or safety-critical systems - advantage.

·       Experience with TensorRT, CUDA, runtime optimization, or embedded deployment - advantage

·       Experience deploying algorithms to edge devices, embedded platforms, or real-time systems - advantage.

·       Experience with Linux, Git, and collaborative development workflows.

·       Experience building data-processing, evaluation, automation, or experiment-management pipelines is an advantage.

Skills

·       Applied perception engineering mindset — able to turn real-world sensing challenges into practical, testable, and deployable solutions.

·       Independent, accountable, and proactive, with a strong “own-it” mindset.

·       Strong problem-solving skills and a structured approach to debugging and validation.