AI Optimization Engineer, Edge & Embedded Systems

Role Title: AI Optimization Engineer, Edge & Embedded Systems
Location: Arlington, VA (Primarily On-site, Hybrid Flexibility Available)
Employment Type: Full-Time

Overview:
A pioneering organization at the intersection of advanced autonomy and national security is seeking a driven AI Optimization Engineer to spearhead the deployment of machine learning models in mission-critical edge environments. From UAVs and aerospace payloads to mobile intelligence systems, this role is vital in enabling real-time AI inference in highly constrained environments—where compute, power, and bandwidth are limited but reliability is non-negotiable.

What You’ll Do:

• Develop and deploy high-performance AI and machine learning models tailored for embedded and low-power platforms.
• Convert, compress, and optimize models using advanced tools such as ONNX, TensorRT, OpenVINO, and TVM.
• Partner with internal R&D teams to transition models from development to real-world applications, bridging the gap between lab innovation and field execution.
• Enhance neural networks for real-time inference by tuning performance, memory use, and energy efficiency.
• Integrate AI systems with sensors, controllers, and autonomy frameworks for onboard deployment in disconnected and resource-constrained environments.
• Perform rigorous profiling, performance benchmarking, and field testing across diverse embedded platforms.
• Troubleshoot system-level issues and iterate solutions in dynamic, operational conditions.

What You Bring:

• Bachelor’s, Master’s, or PhD in Computer Engineering, Electrical Engineering, Computer Science, or a related discipline.
• 3+ years of experience in deploying AI models to embedded, edge, or real-time platforms.
• Strong skills in C++, Python, and embedded computing frameworks.
• Expertise in AI model deployment pipelines including conversion, quantization, pruning, and inference optimization.
• Experience with hardware accelerators such as NVIDIA Jetson, Intel Movidius, Coral, or FPGAs.
• Understanding of embedded system constraints including latency, thermal budgets, and memory management.

Preferred Experience:

• Exposure to autonomous systems such as drones, field robotics, or aerospace solutions.
• Hands-on experience with ROS/ROS2 or robotic middleware integration.
• Familiarity with secure, disconnected, or air-gapped environments with intermittent connectivity.
• Eligibility for or possession of a U.S. security clearance is strongly preferred.

Why Apply:

• Be part of a high-caliber team deploying AI where it matters most—in the field.
• Work on impactful projects supporting critical missions in national defense and security.
• Shape the future of AI-powered autonomy and real-time decision-making at the tactical edge.
• Enjoy a competitive compensation package, growth opportunities, and the support of a cutting-edge engineering culture.