Machine Learning Engineer

Join True Anomaly, a pioneering company in space security. As a Machine Learning Engineer, you will contribute to the design and development of machine learning and AI capabilities. You will work alongside experienced engineers, support the development of models and pipelines, assist in data ingestion and preprocessing, run experiments, and write clean, documented Python code. You will also have access to comprehensive healthcare, generous time off, equity options, 401k plans, 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 l'entraînement 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.

• Foundational understanding of machine learning concepts including supervised learning, unsupervised learning, and model evaluation
• Strong mathematical fundamentals in linear algebra, statistics, and probability
• 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
• Exposure to ML frameworks such as PyTorch, TensorFlow, or JAX through coursework, research, or personal projects
• Proficient in Python
• Bachelor's degree in computer science, machine learning, data science, electrical engineering, or a similar discipline
• You are curious, driven, and eager to grow — someone who takes ownership of their work and isn't afraid to tackle hard problems
• Familiarity with classification, regression, clustering, or anomaly detection techniques
• Internship, research, or project experience applying ML to real-world or research datasets
• Experience with version control (Git) and basic software engineering practices
• Exposure to MLOps concepts such as experiment tracking or model versioning
• Coursework or project work in deep learning, computer vision, or time-series analysis
• Work environment—the work environment; temperature, noise level, inside or outside, or other factors that will affect the person's working conditions while performing the job
• Physical demands—the physical demands of the job, including bending, sitting, lifting and driving