Note: The job is a remote job and is open to candidates in USA. Arcfield is a strategic technology consulting company seeking a Lead Edge AI/ML Engineer to design, optimize, and deploy advanced AI/ML capabilities for tactical edge systems. The role involves leading the development of onboard AI/ML capabilities and collaborating with various engineering teams to ensure mission continuity without relying on continuous human intervention.
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
Skills
- 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)
- Must be able to obtain and maintain a U.S. DoD Secret Security Clearance
Benefits
- Health Insurance
- Life Insurance
- Paid Time Off
- Holiday Pay
- Short Term and Long-Term Disability
- Retirement and Savings
- Learning and Development opportunities
- Wellness programs
- Other optional benefit elections
Company Overview