Delivering decisive capabilities for space superiority. Backed by Accel and Menlo.
About the role
You'll work on hybrid perception systems combining classical computer vision with modern deep learning for autonomous spacecraft: building multi-object tracking pipelines that fuse neural network detections with Kalman filtering, developing coordinate transformation chains from pixels to orbital frames, training models on synthetic space imagery, and deploying algorithms onboard under strict compute/power constraints.
What they're looking for
- 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
More about this role
Space is a warfighting domain. True Anomaly seeks those with the talent and ambition to build the technology that secures it.
OUR MISSION
True Anomaly delivers decisive capabilities for space superiority. We build autonomous spacecraft, advanced payloads, mission software, and space-based interceptors — enabling the U.S. and its Allies to secure the space environment and counter threats from the ultimate high ground.
OUR VALUES
- Be the offset. We create asymmetric advantages with creativity and ingenuity.
- What would it take? We challenge assumptions to deliver ambitious results.
- It’s the people. Our team is our competitive advantage and we are better together.
YOUR MISSION
You'll work on hybrid perception systems combining classical computer vision with modern deep learning for autonomous spacecraft: building multi-object tracking pipelines that fuse neural network detections with Kalman filtering, developing coordinate transformation chains from pixels to orbital frames, training models on synthetic space imagery, and deploying algorithms onboard under strict compute/power constraints.
Your work enables spacecraft to detect objects against star fields, track multiple targets...
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