Machine Learning Engineer (5-8 yrs)

Were going to the Moon. Think youve got what it takes

At Advanced Space were enabling humanitys return to the Moon and building the technologies that will take us to Mars and beyond. Were looking for a Machine Learning Engineer with 58 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. Youll take ownership of technically complex projects from problem formulation and model development through quantitative evaluation integration and operational deployment. Youll work with modern machine learning techniques including statistical learning probabilistic modeling optimization deep learning and reinforcement learning to solve real-world aerospace challenges.

Were 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 NASAs CAPSTONE mission and the Prime Contractor for AFRLs Oracle mission were 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 youre passionate about solving challenging problems and seeing your work fly in space youll 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 edge-case and off-nominal performance. Identify data leakage distribution shifts and other factors that could impact model reliability. Use quantitative analysis and experimentation to validate model performance and inform technical decisions.

Integrate ML capabilities into aerospace systems.

Collaborate with navigation mission design flight software systems engineering and operations teams to integrate machine learning solutions into broader engineering architectures and workflows. Ensure ML capabilities align with mission requirements system constraints and operational needs.

Research and apply emerging technologies.

Read synthesize and apply relevant technical literature emerging research and innovative methodologies in machine learning optimization and autonomy. Evaluate new approaches and identify opportunities to advance Advanced Spaces technical capabilities.

Communicate technical findings and recommendations.

Document technical approaches assumptions results limitations and recommendations. Present findings through design reviews technical documentation and stakeholder discussions translating complex ML concepts into clear actionable insights for multidisciplinary teams.

Leverage modern AI-assisted engineering tools.

Use company-approved AI-assisted and agentic engineering tools responsibly to support software development documentation research and analysis. Critically evaluate generated outputs and apply appropriate security source-provenance reproducibility and technical-validation practices.

• You have a B.S. in Computer Science Machine Learning Software Engineering Aerospace Engineering or another relevant engineering physical-science or quantitative discipline. Equivalent relevant experience may be considered.

• You have 58 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 including version control code reviews automated testing debugging and performance profiling.

• You have experience with at least one modern ML framework such as PyTorch JAX TensorFlow or an equivalent including developing custom models loss functions data pipelines training loops and inference workflows.

• You understand common machine learning model families including neural network architectures and can select or adapt approaches based on data availability computational constraints mission requirements and operational risk.

• You have experience developing reproducible end-to-end ML workflows including data validation experiment tracking model and data versioning integration testing and model evaluation.

• You have working knowledge of at least one aerospace domain such as astrodynamics spacecraft systems navigation mission design or flight and ground software or the ability to rapidly build expertise in these areas.

• You are familiar with machine learning applications for physical or engineered systems reinforcement learning or decision-making methods.

• 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.

• 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.

• Delivering validated reliable ML capabilities that address mission and engineering requirements.

• Developing and integrating machine learning solutions that support spacecraft autonomy navigation mission planning and operations.

• Building reproducible ML workflows that improve model development testing evaluation and deployment.

• Demonstrating measurable model performance through rigorous experimentation and quantitative evaluation.

• Effectively integrating ML capabilities into broader aerospace systems and engineering workflows.

• Taking ownership of complex technical challenges and delivering solutions from initial concept through implementation and operational handoff.

• Communicating technical findings limitations risks and recommendations clearly to engineering teams and stakeholders.

• Contributing to the continued growth of Advanced Spaces machine learning autonomy and engineering capabilities.

• Work on real missions that are shaping the future of lunar and deep-space exploration.

• Develop machine learning and autonomy solutions that support spacecraft operations and next-generation space technologies.

• Collaborate with experts in machine learning navigation mission design and aerospace engineering.

• Apply cutting-edge ML techniques to challenging real-world engineering problems.

• Be part of a growing company where your technical contributions have a direct impact on mission success.

• Join a team thats passionate about delivering innovation to orbitand beyond.

• 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

Advanced Space is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive workplace for all employees. Employment decisions are made without regard to race color religion sex national origin age disability veteran status or any other protected characteristic under applicable law.