Machine Learning Engineer (5-8 yrs)

📋 Job Overview

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.

🏢 About Advanced Space

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.

🎯 The Role

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.

✅ Key Responsibilities

• Develop machine learning solutions for complex engineering challenges.
• Design and implement ML-enabled capabilities.
• Own technically complex projects from concept to deployment.
• Build reliable and reproducible ML workflows.
• Evaluate model performance and validate results.
• Integrate ML capabilities into aerospace systems.
• Research and apply emerging technologies.
• Communicate technical findings and recommendations.
• Leverage modern AI-assisted engineering tools.

📌 Required Qualifications

• You have a B.S. in Computer Science, Machine Learning, Software Engineering, Aerospace Engineering, or another relevant engineering, physical-science, or quantitative discipline.
• You have 5–8 years of professional experience developing and integrating machine learning, optimization, statistical, or data-driven engineering capabilities.
• You have demonstrated experience owning technical problems from initial formulation through implementation, quantitative evaluation, documentation, and stakeholder communication.
• You are proficient in Python and modern software engineering practices.
• You have experience with at least one modern ML framework, such as PyTorch, JAX, TensorFlow, or an equivalent.
• You understand common machine learning model families.
• You have experience developing reproducible, end-to-end ML workflows.
• You have working knowledge of at least one aerospace domain.
• You have experience with machine learning applications for physical or engineered systems.
• You can effectively communicate complex technical concepts and collaborate with multidisciplinary engineering teams.
• You take ownership of your work, approach challenges with curiosity, and are comfortable navigating technical ambiguity.

⭐ Desirable Experience

• An M.S. or Ph.D. in Aerospace Engineering, Computer Science, Artificial Intelligence, Robotics, or a related field.
• Applying model-based or model-free reinforcement learning, model predictive control (MPC), Markov decision processes (MDPs), partially observable Markov decision processes (POMDPs), or hybrid planning approaches to physical systems.
• Developing autonomy architectures spanning perception, estimation and navigation, planning and scheduling, control, and fault management.
• Building simulation or digital-twin environments for model development, evaluation, and sim-to-real transfer.
• Integrating ML-enabled capabilities into guidance, navigation, and control (GN&C), mission design, navigation, flight software, or systems-engineering workflows.
• Applying optimization, probability, statistics, and rigorous experimental design to complex engineering problems.
• Using probabilistic modeling or Bayesian inference to address engineering challenges.
• Communicating complex technical concepts across machine learning, navigation, mission design, flight software, operations, and systems engineering.
• Using agentic AI tools to support engineering workflows while maintaining technical accuracy, security, and reproducibility.

🎁 Benefits

• Base Salary: $124K - $171K (based on experience, qualifications, and location)
• Signing bonus
• Quarterly performance bonuses
• Company-sponsored medical benefits and 401(k)
• Flexible time off
• Relocation assistance

🛂 Visa & Eligibility

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