Must be a U.S. Citizen
Secret Clearance preferred
This position will to support mission-critical aerospace and defense programs. As an Applied Machine Learning Engineer, you will support informed decision-making around the application of machine learning and AI models in safety- and reliability-constrained systems. This role focuses on evaluating tradeoffs between retrieval-based approaches, fine-tuning, targeted training, and non-ML solutions. The position is onsite in Chandler, AZ and works closely with software, infrastructure, simulation, and GNC engineering teams.
Requirements
· Bachelor’s degree in Computer Science, Engineering, Mathematics, or related STEM field
· 3+ years of applied machine learning experience with production systems
· Demonstrated experience making technical tradeoff decisions around:
· RAG vs fine-tuning vs lightweight adaptation
· Model scope, training data selection, and evaluation
· Strong understanding of model failure modes, overfitting, and distribution shift
· Experience deploying ML in environments where correctness and reliability matter
· Ability to clearly communicate ML risks and limitations to non-ML engineers
· U.S. Citizenship
· Preferred Qualifications
· Experience with simulation, autonomous systems, aerospace, or defense programs
· Exposure to guidance, navigation, or control systems
· Experience working with hybrid ML + classical systems
· Familiarity with regulated or compliance-driven software environments
· Active or prior DoD security clearance
Responsibilities
· Evaluate when machine learning should or should not be applied to engineering problems
· Advise teams on tradeoffs between RAG, fine-tuning, and targeted model training
· Support definition of heuristics for model selection, evaluation, and retraining
· Identify and mitigate ML failure modes in system and simulation contexts
· Collaborate with GNC, software, and infrastructure teams on safe ML integration