Software Engineer I, Full Stack (New Grad)

Denver, COfull timeOn Site

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Trueanomalyinc Software Engineer I, Full Stack (New Grad) at True Anomalyinc develops hybrid perception systems for autonomous spacecraft. The role includes implementing tracking algorithms and deploying models onboard spacecraft.

Responsibilities

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

Qualification

Proficiency in PythonUnderstanding of linear algebraStrong debugging skillsExperience with Extended Kalman FiltersUnderstanding of coordinate framesExperience with OpenCVCoursework in optimal estimation

Required

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

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

Benefits

Vision

Company

Trueanomalyinc

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Source: greenhouse

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