Machine Learning Engineer (Space Edge Deployment)

Join True Anomaly, a pioneering company in space security. As a Machine Learning Engineer, you will contribute to the design and development of AI capabilities for space domain awareness. You will work alongside experienced engineers, support the development of ML models, and assist in data preprocessing and feature engineering. Enjoy benefits such as 100% employer-sponsored healthcare, generous time off, equity options, 401k, and learning and development opportunities.

• Contribuer à la conception et au développement de capacités d'apprentissage automatique et d'IA pour True Anomaly.
• Assister au développement, à la formation et à l'évaluation de modèles d'apprentissage automatique pour des tâches pertinentes.
• Écrire du code Python propre, documenté et testable dans le cadre d'une équipe d'ingénierie collaborative.

• You are curious, driven, and eager to grow — someone who takes ownership of their work and isn't afraid to tackle hard problems
• Strong mathematical fundamentals in linear algebra, statistics, and probability
• Exposure to ML frameworks such as PyTorch, TensorFlow, or JAX through coursework, research, or personal projects
• Proficiency in Python
• Eagerness to learn, take feedback, and grow in a fast-paced, mission-driven environment
• Passion for spaceflight and advancing capabilities related to space domain awareness and space security
• Bachelor's degree in computer science, machine learning, data science, electrical engineering, or a similar discipline, plus 2-4 years of experience; or a Master's degree in one of these fields with no experience required
• Foundational understanding of machine learning concepts including supervised learning, unsupervised learning, and model evaluation
• To conform to U.S. Government space technology export regulations, including the International Traffic in Arms Regulations (ITAR) you must be a U.S. citizen, lawful permanent resident of the U.S., protected individual as defined by 8 U.S.C. 1324b(a)(3), or eligible to obtain the required authorizations from the U.S. Department of State
• Internship, research, or project experience applying ML to real-world or research datasets
• Experience with version control (Git) and basic software engineering practices
• Familiarity with classification, regression, clustering, or anomaly detection techniques
• Coursework or project work in deep learning, computer vision, or time-series analysis
• Exposure to MLOps concepts such as experiment tracking or model versioning