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# Software Engineer I, Perception (New Grad)

- **Company:** True Anomaly
- **Location:** Denver, CO, United States
- **Experience:** Starter
- **Salary:** $75,000.0 - $80,000.0
- **Contract:** Internship / Graduate position
- **Skills:** Training Data, Artificial Neural Networks, Computer Vision, C++ (Programming Language), Software Code Optimization, Software Debugging, Python (Programming Language), Object Detection, OpenCV, Tensorflow, Sensor Fusion, Software Engineering, Visual Systems, Component Analysis, Pytorch, Deep Learning, Convolutional Neural Networks, Information Technology, ONNX (Open Neural Network Exchange) Format, Machine Learning Operations, Front End Software Development, TensorRT, Feature Extraction, Data Generation
- **Published:** October 27, 2026
- **Apply:** https://www.careerbuilder.com/job-details/software-engineer-i-perception-new-grad-denver-co--52f9c340-a75b-48b2-ab18-8210a04223a5

## About the Role

Software Engineering, Wheel/Front-End Loader, Healthcare, Fitness, Reimbursement, Algorithms, Kalman Filter, Deep Learning, Neural Networks, Computer Vision, * Currently pursuing or recently completed Bachelor's or Master's degree in computer science, electrical engineering, robotics, aerospace engineering, or related technical field

 * Coursework in both computer vision and estimation theory (or willingness to learn both)
 * Proficiency in Python; some exposure to C++ (we'll teach you more)
 * Familiarity with either classical tracking (Kalman filters, data association) OR deep learning (PyTorch, training neural networks)
 * Understanding of linear algebra, probability, and coordinate transformations
 * Ability to read research papers from both robotics (ICRA, IROS) and ML venues (CVPR, NeurIPS) and implement algorithms
 * Strong debugging skills: tracking down lost tracks, numerical instability, and model failure modes
 * Eagerness to learn the intersection of classical perception and modern ML
 * U.S. Citizen (required for facility access and government contracts)

PREFERRED SKILLS AND EXPERIENCE

 * Experience with Extended Kalman Filters, multi-object tracking, or state estimation
 * Familiarity with neural network training in PyTorch or TensorFlow: object detection (YOLO, Faster R-CNN), classification, or segmentation
 * Understanding of coordinate frames: camera intrinsics/extrinsics, quaternions, rotation matrices, ECI/LVLH/RIC frames
 * Exposure to model optimization for edge deployment: quantization (INT8, FP16), ONNX, TensorRT
 * Experience with OpenCV, image processing pipelines, or classical feature extraction
 * Coursework in optimal estimation, sensor fusion, or probabilistic robotics (Kalman/particle filters)
 * Prior work with synthetic data generation, Blender/Unreal for rendering, or domain randomization
 * Understanding of data association algorithms: Hungarian algorithm, auction algorithm, JPDA
 * Familiarity with tracking-by-detection pipelines: detection association update track management
 * Experience debugging visual systems: false positives, missed detections, track ID switches, covariance tuning
 * Prior internship or project deploying algorithms to embedded systems (Jetson, mobile, ROS)
 * Exposure to multi-modal perception: fusing camera + IMU, camera + lidar, or learned sensor fusion

## Description

Space is a warfighting domain. True Anomaly seeks those with the talent and ambition to build the technology that secures it., * Implement classical tracking algorithms: Extended Kalman Filters for state estimation, Hungarian algorithm for data association, track management logic (tentative/confirmed/coasted tracks)

 * Train neural networks for detection and classification: YOLO for object detection, ResNet-based classifiers for threat discrimination, learned appearance features for re-identification
 * Build hybrid perception pipelines: neural network detections classical tracking coordinate transformations angle-only measurements for navigation
 * Develop image processing chains: hot pixel filtering, adaptive thresholding, centroiding, connected component analysis, star catalog matching
 * Deploy models to edge hardware: quantize neural networks (INT8), integrate with C++ inference engines (ONNX Runtime, TensorRT), optimize for space-qualified processors
 * Implement coordinate transformations: pixel camera frame body frame Earth-Centered Inertial (ECI), accounting for lens distortion and attitude uncertainty
 * Generate synthetic training data: render spacecraft in Blender with domain randomization (lighting, attitudes, backgrounds), create labeled datasets for rare scenarios
 * Validate end-to-end performance: software-in-the-loop simulation, processor-in-the-loop testing, hardware-in-the-loop with real camera feeds