Machine Learning Engineer

YOUR MISSION

As a member of the Applied Algorithms and Autonomy team, you will contribute to the design and development of machine learning and AI capabilities for True Anomaly. Working alongside experienced engineers, you will support the development of models and pipelines that enable object classification, anomaly detection, and data-driven decision-making. You are curious, driven, and eager to grow — someone who takes ownership of their work and isn't afraid to tackle hard problems.

• →Assist in the development, training, and evaluation of ML models across a range of mission-relevant tasks
• →Support data ingestion, preprocessing, and feature engineering pipelines
• →Run experiments, track results, and contribute to model evaluation and iteration
• →Write clean, documented, and testable Python code as part of a collaborative engineering team
• →Learn and grow alongside senior engineers, contributing meaningfully from day one

• Bachelor's degree in computer science, machine learning, data science, electrical engineering, or a similar discipline
• Proficient in Python
• Foundational understanding of machine learning concepts including supervised learning, unsupervised learning, and model evaluation
• Exposure to ML frameworks such as PyTorch, TensorFlow, or JAX through coursework, research, or personal projects
• 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

• Internship, research, or project experience 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
• Coursework or project work in deep learning, computer vision, or time-series analysis

• Work Location— this is a fully onsite role. Candidates must be based in or able to commute to our Denver or Long Beach office daily. 
• 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.

This position will be open until it is successfully filled. To submit your application, please follow the directions below. #LI-Onsite