Machine Learning Engineer

We’re going to the Moon. Think you’ve got what it takes?

At Advanced Space, we're enabling humanity's return to the Moon and building the technologies that will take us to Mars and beyond. We're looking for a Machine Learning Engineer with 5–8 years of experience to develop innovative ML-driven capabilities that support spacecraft missions, autonomy, navigation, mission planning, and advanced engineering solutions.

This is a hands‑on technical role focused on translating complex mission and engineering challenges into practical, data‑driven solutions. You'll take ownership of technically complex projects from problem formulation and model development through quantitative evaluation, integration, and operational deployment. You'll work with modern machine learning techniques, including statistical learning, probabilistic modeling, optimization, deep learning, and reinforcement learning, to solve real‑world aerospace challenges.

We're looking for someone who enjoys tackling ambiguous technical problems, has a strong foundation in machine learning and software engineering, and is excited to collaborate across disciplines to develop capabilities that support real space missions.

This position is open to U.S. Persons (U.S. citizens or lawful permanent residents) only. Visa sponsorship is not available.

Advanced Space exists to enable the sustainable exploration, development, and settlement of space through innovative software, mission services, and technology solutions. As the owner and operator of NASA’s CAPSTONE™ mission and the Prime Contractor for AFRL’s Oracle mission, we’re helping shape the future of cislunar exploration while supporting commercial, civil, and national security customers.

Our team combines deep technical expertise with an entrepreneurial mindset. We move quickly, collaborate across disciplines, and empower every engineer to make meaningful contributions. If you’re passionate about solving challenging problems and seeing your work fly in space, you’ll fit right in.

Develop machine learning solutions for complex engineering challenges.

Translate mission, operations, and engineering needs into well‑defined ML and data‑driven problems. Establish success metrics, baselines, datasets, evaluation plans, and quantitative acceptance criteria to develop solutions that address real‑world mission requirements.

Design and implement ML-enabled capabilities.

Select, develop, evaluate, and maintain machine learning solutions that support spacecraft mission planning, operations, autonomy, navigation, physical‑system modeling, signal extraction, and internal engineering workflows. Apply appropriate methods based on mission needs, data availability, computational constraints, and operational requirements.

Own technically complex projects from concept to deployment.

Take ownership of technical work packages from initial problem formulation through implementation, integration, documentation, and operational handoff. Define technical approaches, assess trade‑offs, identify risks, and communicate architectural decisions and recommendations to stakeholders.

Build reliable and reproducible ML workflows.

Develop and maintain end‑to‑end machine learning workflows, including data curation and validation, experiment tracking, model and data versioning, configuration management, automated regression testing, and performance monitoring. Apply modern software engineering practices to ensure solutions are maintainable, scalable, and reliable.

Evaluate model performance and validate results.

Design rigorous evaluation strategies and domain‑appropriate metrics to assess nominal,…