Aerospace & Defense startup currently in Stealth based in El Segundo, made up of former SpaceX, Anduril, and Lockheed engineers, applying their knowledge to produce life-saving systems. Backed by joint staff level military leadership and several major SV firms. Our first product is designed for immediate deployment around the world.

Job Overview

We are seeking a Perception Engineer with expertise in sensor fusion and computer vision and to develop target detection, tracking, and classification capabilities for a variety of systems. This role requires a deep understanding of optical, infrared, and radar-based perception, as well as geometric vision, image processing, and real-time tracking algorithms.

The ideal candidate will have strong experience in machine learning, sensor modeling, multi-modal data fusion, and computational imaging techniques that enhance target recognition and tracking under real-world conditions, and would use machine learning classification techniques along with physics-based approaches to ensure high reliability and robustness in contested environments.

Key Responsibilities

• Develop and implement computer vision algorithms for target detection, tracking, and classification using EO/IR, radar, and LIDAR sensor data.
• Design multi-sensor fusion algorithms to improve detection accuracy and reduce ambiguity in real-world scenarios.
• Utilize geometric vision techniques (e.g., epipolar geometry, 3D reconstruction, optical flow, structure from motion) to enhance tracking and target identification.
• Develop and refine atmospheric and environmental compensation models to improve EO/IR sensor performance in adverse conditions (e.g., fog, smoke, turbulence, low-light).
• Implement real-time image processing techniques, including contrast enhancement, noise reduction, feature extraction, and edge detection, to improve sensor data fidelity.
• Develop AI/ML models for friend/foe classification and target type identification (e.g., vehicle, personnel, aircraft).
• Design object tracking algorithms based on deterministic methods such as Kalman filtering, particle filtering, and optical flow-based tracking.
• Model target kinematics and dynamics to enhance predictive tracking and classification.
• Work with hardware engineers to integrate real-time perception algorithms into embedded systems and ensure low-latency processing.
• Perform simulation-based testing and field validation using high-fidelity sensor models and real-world datasets.
• Develop and test friend/foe classification and target type identification algorithms using a variety of data-driven ML models.
• Optimize algorithms for real-time performance on low-SWaP (Size, Weight, and Power) platforms such as FPGA or GPU-accelerated embedded systems.
  
  

Required Qualifications

• M.S. or Ph.D. in Computer Science, Electrical Engineering, Applied Physics, Aerospace Engineering, or a related field.
• 3+ years of experience in computer vision, sensor fusion, and real-time image processing for defense, robotics, or aerospace applications.
• Strong background in optics, infrared imaging, radar signal processing, and multispectral/hyperspectral imaging.
• Experience with physics-based image formation models, including camera calibration, radiometric corrections, and optical distortion modeling.
• Expertise in object tracking techniques, including motion models, optical flow, and deterministic state estimation.
• Proficiency in programming languages such as C++, Python, and MATLAB for real-time applications.
• Hands-on experience with embedded systems and real-time optimization for edge computing platforms.
• Experience integrating multiple sensing modalities (EO, IR, LiDAR, radar, RF) into a cohesive perception system.
• Strong mathematical foundation in linear algebra, signal processing, machine learning, and probabilistic estimation.
  
  

Preferred Qualifications

• Experience in missile guidance, UAV autonomy, or defense-related sensor systems.
• Understanding of signature-based target classification methods, including thermal and radar cross-section (RCS) analysis.
• Hands-on experience with ROS (Robot Operating System) for perception and sensor integration.
• Familiarity with synthetic aperture radar (SAR) processing and passive RF-based target detection.
• Familiarity with synthetic data generation and simulation tools such as CARLA, Gazebo, or Unreal Engine.
• Experience with numerical modeling of sensor behavior and adaptive optics techniques.
• Background in high-speed image processing and real-time embedded vision system design.