LEAD ANALYST - LEAD ENGINEER - PRINCIPAL ENGINEER - AI & Machine Learning Intelligent Avionics

Who We Are:

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.

Objectives of this Role:

• 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.
  
  

Daily and Monthly Responsibilities:

• 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.
  
  

Requirements:

• 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, TensorFlow, 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.
  
  

Special Requirements: 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. Job Locations: San Antonio, Texas Or Oklahoma City, Oklahoma