Machine Learning Engineer

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The team at Advanced Space is leading humanity back to the Moon and pioneering innovative solutions in the space industry. Based in Westminster, Colorado, we seek a Machine Learning Engineer eager to push the state of the art in aerospace.

ML Engineers at Advanced Space work closely with engineers in other disciplines (aerospace, systems). This position is responsible for designing, building, and testing machine learning models and systems that solve real‑world problems in spacecraft autonomy, anomaly detection, natural language processing, uncertainty quantification, and multimodal signal detection & characterization. The role is headquartered in our Westminster, Colorado offices and is primarily on‑premises.

• Apply ML first principles and engineering intuition to projects such as fast & accurate models of physical systems, spacecraft navigation, spacecraft autonomy, signal extraction from noisy data, natural language processing, internal company tools, multi‑agent reinforcement learning, and approximate optimal maneuver design.
• Lead development of advanced ML models and AI‑driven solutions.
• Research and implement emerging ML technologies and techniques.
• Improve model interpretability, scalability, and efficiency.
• Contribute to strategic decision‑making and technical roadmaps.
• Work with interdisciplinary, cross‑functional teams to apply ML/AI algorithms to a variety of problems in the space industry.
• Find, digest and apply knowledge from research publications to assigned tasks.
• Stay up‑to‑date on the latest research by reading technical papers and attending conferences.
• Participate in technical interviews.

• B.S. in Computer Science, Machine Learning, Software Engineering, Aerospace Engineering, or another physical science/engineering discipline, or equivalent work experience.
• 5–8 years of relevant experience.
• Ability to handle a broad range of tasks, from bug fixes to feature development.
• Experience in software/algorithm component design.
• Basic understanding of aerospace engineering.
• Strong understanding of algorithms, data structures, and system design principles.
• Specialization in certain areas and contribution to process details.
• Solid foundation in neural networks (feed forward, recurrence, convolution, attention).
• Ability to understand and implement new methods.
• Familiarity with reinforcement learning, supervised/unsupervised learning, PCA, clustering, regression, classification.
• Understanding of system design and architecture; ability to apply cross‑domain methods creatively.
• Confidence in writing a neural network model from scratch.
• Effective use of tools to track model training.
• Knowledge of train/test/validation splits and model evaluation.
• Ability to identify suspiciously good model results.
• Proficiency with PyTorch, Tensor Flow, Jax, or equivalent.

• Proficiency in an engineering field (aerospace or another).
• Confidence in deploying a model to a server or edge device.

• Base Salary Range – $122,284 - $144,284 based on role, experience, and location.
• Signing bonus.
• Quarterly performance bonuses.
• Company–provided health insurance and 401(k) plan upon eligibility.
• Unlimited vacation time and extensive flexibility.
• Relocation assistance.

• US Person.
• Security clearance not needed.

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