Lead Edge AI/ML Engineer

Responsibilities

• Architect Edge AI Pipelines: Lead the end-to-end development of machine learning pipelines, from data curation and model training to final deployment on low-SWaP edge inference accelerators (GPUs, NPUs, FPGAs).
• Build the Agentic Watchdog: Design and deploy a highly autonomous reinforcement learning or anomaly-detection agent to predict, detect, and instantly clear hardware or software faults.
• Enhance AI Navigation Fusion: Collaborate directly with PNT engineers to integrate ML into the state estimation loop, using neural networks to classify NAVWAR spoofing attacks, model complex inertial sensor noise, or fuse intermittent visual/RF data.
• Bridge the AI/Embedded Gap: Partner with embedded C++ and DSP engineers to translate heavy PyTorch/TensorFlow models into highly optimized, deterministic C++ inference engines using TensorRT, ONNX Runtime, or edge-specific SDKs.
• Optimize for SWaP: Execute extreme model quantization (INT8, FP16), pruning, and knowledge distillation to ensure AI models don't exceed strict memory, thermal, and compute latency budgets.
• Lead the Technical Vision: Define the ML architecture for the program, manage junior engineers/data scientists, and interface directly with end-customers/stakeholders during capability demonstrations.

Qualifications

• BS 8-10, MS 6-8, PhD 3-5 (degree in Computer Science, Machine Learning, Robotics, Electrical Engineering, or a related technical field).
• Experience developing and deploying machine learning models to production environments, with a strong focus on Edge AI or embedded systems.
• Fluency in Python (for training/architecture) and modern C++ (for edge deployment and embedded integration).
• Deep expertise with ML optimization frameworks and runtimes (e.g., TensorRT, ONNX, TFLite, OpenVINO) targeting edge hardware (like NVIDIA Jetson, Coral, or Xilinx SoCs).
• Demonstrated experience developing autonomous agents, anomaly detection algorithms, or reinforcement learning systems applied to complex hardware/software ecosystems.
• Proven ability to collaborate intimately with embedded software, DSP, or systems engineers to deploy AI into real-time, deterministic systems.
• Familiarity with hardware-in-the-loop (HITL) testing and CI/CD pipelines for machine learning models (MLOps).

Equal Pay Act

This is the projected compensation range for this position. There are differentiating factors that can impact a final salary/hourly rate, including, but not limited to, Contract Wage Determination, relevant work experience, skills and competencies that align to the specified role, geographic location (For Remote Opportunities), education and certifications as well as Federal Government Contract Labor categories. In addition, Arcfield invests in its employees beyond just compensation. Arcfield's benefits offerings include, dependent upon position, Health Insurance, Life Insurance, Paid Time Off, Holiday Pay, Short Term and Long-Term Disability, Retirement and Savings, Learning and Development opportunities, wellness programs as well as other optional benefit elections.

EEO Statement

We are an equal opportunity employer and federal government contractor. We do not discriminate against any employee or applicant for employment as protected by law.