Senior AI​/ML Engineer – Air Vehicle Systems

Overview

What if the next leap in air dominance doesn’t come from a single breakthrough, but from hundreds of smarter decisions, made faster, across engineering, testing, and operations?

At Lockheed Martin Aeronautics, our Aero AI team builds trusted artificial intelligence that strengthens deterrence, accelerates engineering insight, and directly supports the warfighter. We don’t chase AI for its own sake. We apply it deliberately, where it meaningfully improves performance, survivability, and decision advantage.

This role is for a senior, self-directed AI professional who can operate with a high degree of autonomy. Someone who doesn’t wait to be handed a problem, but instead seeks out opportunity, determines where AI adds value (and where it doesn’t), and delivers end-to-end solutions with minimal oversight.

You'll work alongside air vehicle engineers to elevate how aircraft are designed, tested, sustained, and operated while also enabling intelligent onboard capabilities such as health management, autonomy support, and decision augmentation. If you're energized by ownership, trust, and real mission impact, and you’re comfortable being given the reins and expected to run, this role was built for you.

This role supports the Air Vehicle Engineering function by independently identifying, scoping, and executing AI/ML solutions that streamline engineering workflows and enable intelligent onboard capabilities. The engineer is expected to operate with limited direction, framing problems, assessing AI suitability, and delivering results across the full lifecycle.

• Independently identify, scope, and execute AI opportunities across engineering workflows and onboard systems, including determining when AI is not the appropriate solution
• Design, develop, test, and deploy AI/ML models optimized for real-time or resource-constrained environments
• Collaborate with air vehicle engineering teams to understand problem spaces and identify opportunities for efficiency, insight, or automation
• Engage across line-of-business (LOB) teams to prototype and pilot AI-enhanced workflows
• Develop AI-based approaches for anomaly detection, decision support, or health management
• Work with embedded systems, vehicle software, and autonomy-focused teams to integrate AI solutions
• Conduct experiments using flight test data, onboard telemetry, simulation environments, and engineering datasets
• Ensure algorithmic performance meets constraints related to compute, power, latency, and reliability
• Communicate technical tradeoffs, risks, and outcomes clearly to engineering and AI leadership
• Contribute to IRAD efforts, capability roadmaps, and portfolio expansion through hands-on technical delivery
• Mentor other engineers as needed and serve as technical lead for small, project-focused teams

• Proficiency in Python and C++ for AI/ML development
• Demonstrated experience developing and deploying AI/ML solutions end-to-end with minimal supervision
• Strong understanding of supervised and unsupervised learning techniques
• Experience with modern ML frameworks (e.g., PyTorch, Tensor Flow, ONNX)
• Demonstrated ability to independently assess problem spaces and determine whether AI is an appropriate solution
• Bachelor’s degree or higher in Aerospace Engineering, Mechanical Engineering, Computer Science, Electrical Engineering, or related field
• Active Secret clearance (minimum)
• Strong communication skills and ability to work directly with engineers across multiple disciplines

• Experience applying AI to complex engineered systems (aerospace, robotics, autonomy, or similar domains)
• Familiarity with perception systems, sensor fusion, or data-driven anomaly detection
• Experience deploying AI to embedded or operational environments
• Understanding of air vehicle engineering lifecycle, from design through test, integration, and sustainment
• Exposure to AI verification, explainability, or safety-critical considerations
• Experience collaborating across functional organizations and LOB teams
• Familiarity with human-systems integration (HSI) concepts
• Prior contribution to IRAD, technology road mapping, or new capability incubation

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