Applied Machine Learning Engineer

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