Intelligent Edge Engineer

Accountabilities

• Create efficient, secure, and scalable machine learning solutions optimized for deployment on resource-constrained devices.
• Design and deploy edge AI solutions optimized for mobile SoCs, NPUs, embedded accelerators, and specialized hardware platforms.
• Apply model compression techniques including quantization, pruning, and knowledge distillation to meet edge device requirements.
• Optimize machine learning models for latency, memory usage, power consumption, and overall performance.
• Build and maintain inference solutions using frameworks such as TensorFlow Lite, ONNX Runtime, and Core ML.
• Develop optimization strategies for accelerator backends including DSPs, NPUs, and mobile GPUs.
• Create reliable workflows for on-device model updates, version management, staged rollouts, and recovery processes.
• Design hybrid edge-cloud architectures that adapt to device capabilities and connectivity conditions.
• Build privacy-conscious telemetry systems that support continuous model improvement.
• Collaborate with hardware, firmware, product, and engineering teams to align AI solutions with technical constraints.
• Implement secure execution methods, model protection strategies, and integrity verification mechanisms.
• Develop benchmarking frameworks to evaluate accuracy, latency, energy efficiency, and device performance.
• Support responsible AI practices through privacy protection and bias evaluation.
• Maintain technical documentation covering architectures, design decisions, operational procedures, and engineering guidelines.
• Stay informed on advancements in edge AI technologies and recommend improvements based on industry developments.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related technical field.
• Six or more years of experience in machine learning engineering with significant exposure to edge or mobile AI systems.
• Strong programming skills in Python and C++.
• Demonstrated experience with model optimization techniques including quantization, pruning, and compression.
• Experience with edge inference frameworks such as TensorFlow Lite, ONNX Runtime, Core ML, or similar technologies.
• Strong understanding of mobile, embedded, and accelerator-based hardware architectures.
• Proven experience deploying machine learning models into production environments.
• Strong performance profiling and optimization skills.
• Knowledge of on-device security, privacy, and responsible AI considerations.
• Excellent communication skills with the ability to collaborate with technical and non-technical stakeholders.
• Experience with custom NPU or DSP toolchains is preferred.
• Familiarity with federated learning, on-device personalization, safety-critical systems, or industrial edge deployments is a plus.
• Experience optimizing large language models for edge inference is advantageous.

Benefits

• Competitive annual salary range of $100,000-$150,000.
• Full-time employment with career growth opportunities.
• Opportunity to work on advanced AI, cloud, and enterprise technology initiatives.
• Collaborative environment focused on innovation and technical excellence.
• Exposure to emerging edge AI technologies and complex engineering challenges.
• Opportunities to contribute to impactful production-level machine learning solutions.