Senior AI/ML Lead - Embedded Aerospace Systems

Southwest Research Institute; San Antonio (TX); On-site; USD 150,000 - 210,000; Full time

Job summary

Southwest Research Institute seeks a senior AI/ML lead to advance defense avionics through physics-informed and data-driven methods. You will architect and deploy AI on embedded platforms, ensuring real-time performance and seamless hardware integration with sensors and mission software.

The role requires a strong background in Python/C++, ML frameworks, and experience transitioning research into deployable embedded solutions for aerospace environments.

Qualifications

• Requires Bachelor’s or Master’s in Electrical/Computer/Aerospace Engineering or related field.
• Graduate AI/ML or closely related discipline preferred.
• 12+ years progressive engineering experience in AI/ML, data science or autonomy.
• Expert Python and C/C++ development with modern ML frameworks.
• Experience integrating AI with avionics hardware and real-time systems.

Responsibilities

• Design, implement, optimize, and validate AI/ML algorithms for embedded aerospace and avionics applications, including physics-informed ML and sensor fusion.
• Develop production-quality Python and C/C++ software and deploy models to real-time embedded compute platforms; profile latency, memory, power, determinism, and reliability.
• Integrate AI algorithms with avionics hardware, sensors, mission software, and test assets; conduct SIL/HIL experiments and performance assessments.
• Lead technical reviews, trade studies, customer demonstrations, proposals, white papers, and mentoring of engineers.

Skills

Python

C/C++

ML frameworks

Embedded systems

Sensor fusion

Real-time systems

Education

Bachelor's or Master's in Electrical/Computer/Aerospace Engineering

Tools

PyTorch

TensorFlow

JAX

Edge compute hardware

Job description

Southwest Research Institute seeks a senior AI/ML lead to advance defense avionics through physics-informed and data-driven methods. You will architect and deploy AI on embedded platforms, ensuring real-time performance and seamless hardware integration with sensors and mission software.

The role requires a strong background in Python/C++, ML frameworks, and experience transitioning research into deployable embedded solutions for aerospace environments.