LEAD Analyst - LEAD Engineer - Principal Engineer - AI & Machine Learning Avionics

The Strategic Aerospace Department in the Defense & Intelligence Solutions Division provides the USAF with engineering services that increases the warfighters capabilities specifically for the bombers, tankers and heavies platforms.

Lead advanced AI/machine learning (ML) research and development for aerospace mission systems, embedded avionics, intelligent sensing, autonomy, and real-time edge computing across the Department's strategic avionics portfolio. Develop physics-informed and data-driven ML approaches for sensor fusion, system identification, anomaly detection, prediction, decision support, and adaptive mission-system capabilities. Architect, prototype, and transition AI/ML algorithms from research environments into deployable embedded hardware using C/C++, Python, heterogeneous processors, and real-time software frameworks.

Provide senior technical leadership for multidisciplinary integration of AI-enabled hardware/software into complex avionics systems, including laboratory, SIL/HIL, subsystem, and platform-level verification. Advance Department AI capabilities through technical strategy, customer engagement, proposals, publications, technology demonstrations, mentoring, and transition of research into funded aerospace programs.

• Design, implement, optimize, and validate AI/ML algorithms for embedded aerospace and avionics applications, including physics-informed ML, sensor fusion, inference, prediction, classification, and autonomy.
• Develop production-quality Python and C/C++ software and deploy trained models to real-time embedded compute platforms; profile latency, memory, power, determinism, reliability, and mission performance.
• Integrate AI algorithms with avionics hardware, sensors, communications, mission software, and test assets; develop and execute SIL/HIL experiments, data pipelines, verification methods, and performance assessments.
• Collaborate with electrical, embedded software, systems, RF, mechanical, test, cybersecurity, and flight-domain engineers to solve complex AI integration, interface, timing, assurance, and qualification challenges.
• Lead technical reviews, trade studies, experiments, customer demonstrations, proposals, white papers, and technical reports; mentor engineers and establish reusable AI/ML architectures, tools, and engineering practices.

• Requires a Bachelors or a Masters degree in Electrical Engineering, Computer Engineering, Aerospace Engineering, Engineering Physics, Applied Mathematics, Data Science, or related engineering or technical degree field. Graduate degrees in AI or Machine Learning or a closely related discipline are strongly preferred.
• 12+ years:
  Progressive engineering experience developing advanced AI/ML, data science, intelligent systems, or autonomy solutions, with demonstrated technical leadership and successful transition of algorithms into operational hardware/software.
• 12+ years:
  Proven expertise in physics-informed machine learning, scientific ML, system identification, reduced-order modeling, estimation, optimization, uncertainty quantification, or hybrid physics/data-driven methods.
• 12+ years:
  Expert-level Python and C/C++ development with hands-on experience using modern ML frameworks such as PyTorch, Tensor Flow, JAX, or equivalent, and deploying models to embedded CPU/GPU/FPGA or other edge-compute hardware.
• 12+ years:
  Demonstrated aerospace/defense avionics experience integrating AI-enabled capabilities with sensors, mission systems, embedded electronics, real-time interfaces, SIL/HIL test environments, and verification/validation processes.
• A valid/clear driver's license is required.

• Applicant selected will be subject to a government security investigation and must meet eligibility requirements for access to classified information.
• Applicant must be a U.S. citizen.

• San Antonio, Texas
• Oklahoma City, Oklahoma

For more information about this division, visit the Defense & Intelligence Solutions home page.

An Equal Employment Opportunity

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race, color, religion, sex, national origin, disability, and veteran status.