SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Salary: USD 125,000 - 220,000 / annual
True Anomaly is building autonomous spacecraft, advanced payloads, mission software, and space-based interceptors to secure the space environment and counter threats. As a Machine Learning Engineer II on the Applied Algorithms and Autonomy team, you will contribute to the design and development of ML and AI capabilities that enable object classification, anomaly detection, and data-driven decision-making for space domain awareness and space security applications.
You will work alongside experienced engineers to develop, train, and evaluate machine learning models across mission-relevant tasks. Responsibilities include supporting data ingestion, preprocessing, and feature engineering pipelines; running experiments and tracking results; contributing to model evaluation and iteration; and writing clean, documented, testable Python code as part of a collaborative engineering team. You will learn and grow alongside senior engineers while contributing meaningfully from day one.
Required qualifications include a Bachelor's degree in computer science, machine learning, data science, electrical engineering, or similar discipline with 2-4 years of experience (or a Master's degree with no experience required). You must have proficiency in Python, foundational understanding of machine learning concepts (supervised learning, unsupervised learning, model evaluation), exposure to ML frameworks such as PyTorch, TensorFlow, or JAX, and strong mathematical fundamentals in linear algebra, statistics, and probability. You should be eager to learn, take feedback, and grow in a fast-paced, mission-driven environment, with a passion for spaceflight and space security.
Preferred experience includes internship, research, or project work applying ML to real-world or research datasets; familiarity with classification, regression, clustering, or anomaly detection techniques; experience with version control (Git) and basic software engineering practices; exposure to MLOps concepts such as experiment tracking or model versioning; and coursework or project work in deep learning, computer vision, or time-series analysis.
