Intuitive Machines — AI Engineer Behavioral Interview (Engineering)

What this covers: A 60-minute behavioral assessment tailored to Intuitive Machines’ mission-driven, fast-paced aerospace environment. Expect emphasis on ownership under ambiguity, safety-first decision making, collaboration with flight software, GNC, perception, test, and operations teams, and learning agility required to ship autonomy/ML systems for space missions. Real candidate reports indicate a streamlined process (recruiter/HR screen → behavioral/technical video with engineers → manager/panel; sometimes onsite) and quick timelines, with resume deep-dives and pragmatic “how would you solve it” prompts. You may also be asked about learning new skills, prior failures, and comfort with occasional long hours. ([glassdoor.com](https://www.glassdoor.com/Interview/Intuitive-Machines-Interview-Questions-E2097916.htm?utm_source=chatgpt.com)) Cultural context: Reviews consistently reference high-impact work on lunar missions and NASA contracts, small cross-functional teams, and a rapid pace with growth-related “growing pains.” Use STAR, quantify outcomes, and highlight risk management and cross-discipline communication. ([glassdoor.com](https://www.glassdoor.com/Reviews/Intuitive-Machines-Reviews-E2097916.htm?utm_source=chatgpt.com)) Suggested agenda (60 min): - 5 min — Warm-up and role context (team, mission phase, autonomy/ML scope) - 10 min — Ownership under ambiguity: a time you led an AI/ML initiative with shifting requirements or incomplete data - 10 min — Safety and risk: tradeoffs you made to protect mission/safety while delivering to schedule; how you validated models in edge/embedded or resource-constrained settings - 10 min — Collaboration: partnering with systems/flight test to turn research into flight-ready software; handling disagreements; documenting decisions - 10 min — Resilience and pace: navigating tight timelines, handoffs to operations, and post-incident learning; managing stress and rare long hours when milestones demand it. ([glassdoor.com](https://www.glassdoor.com/Interview/Intuitive-Machines-Interview-Questions-E2097916.htm?utm_source=chatgpt.com)) - 10 min — Candidate Q&A Targeted question bank (behavioral, AI Engineer): - Tell us about a time you owned an ML/autonomy component from prototype to mission-critical deployment. What risks did you surface and how did you mitigate them? - Describe a failure in your model or pipeline (e.g., data drift, sensor degradation). How did you detect, triage, and prevent recurrence? - Give an example of reconciling safety constraints with performance when stakeholders pushed for faster delivery. - Walk through a high-stakes design decision you made with incomplete telemetry or test data. How did you communicate uncertainty to flight/test leads? - When have you enabled a non-ML team (e.g., avionics, controls) to successfully use your models? What artifacts (docs, tests, dashboards) did you provide? - Example of learning a new technique or tool quickly to unblock a mission milestone; how do you learn effectively under time pressure? ([glassdoor.com](https://www.glassdoor.com/Interview/Intuitive-Machines-Interview-Questions-E2097916.htm?utm_source=chatgpt.com)) - Tell us about contributing to an incident review/post-mortem. What changed in your processes afterward? - Describe how you ensure reproducibility and traceability (datasets, configs, model cards) for regulated/safety-conscious environments. - How do you decide when to ship a “good enough” model vs. pursue further gains? Who do you align and how? - Share a time you challenged a prevailing technical direction. What was the outcome? What interviewers look for: clear STAR stories; principled decision making under uncertainty; explicit safety/risk thinking; cross-functional communication; bias to action with documentation and testing discipline; humility, resilience, and growth mindset aligned to lunar-mission realities and rapid execution culture. Reports suggest interviews feel conversational yet focused on practical problem-solving and fit for fast-moving schedules. ([glassdoor.com](https://www.glassdoor.com/Interview/Intuitive-Machines-Interview-Questions-E2097916.htm?utm_source=chatgpt.com))