Intelligent edge engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Intelligent Edge Engineer based in United States.

This role focuses on designing and deploying advanced AI solutions that operate directly on edge devices, bringing machine learning capabilities closer to users and real-world environments.

You will develop and optimize models for mobile platforms, embedded systems, and specialized hardware while balancing performance, efficiency, and reliability.

The position offers the opportunity to solve complex engineering challenges involving compute limitations, connectivity constraints, and hardware optimization.

You will collaborate with multidisciplinary teams across engineering, product, and hardware domains to deliver impactful AI-powered experiences.

The ideal candidate will contribute to the evolution of edge intelligence through innovative model optimization, secure deployment practices, and scalable architectures.

This is a remote opportunity for an experienced engineer passionate about building production-ready AI systems beyond traditional cloud environments.

Accountabilities:

The Intelligent Edge Engineer will be responsible for creating efficient, secure, and scalable machine learning solutions optimized for deployment on resource-constrained devices. The role requires strong technical ownership across model optimization, edge architecture, performance engineering, and cross-functional collaboration.

• 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:

The ideal candidate brings strong machine learning engineering expertise combined with hands-on experience deploying AI systems on edge or embedded platforms. Success in this role requires technical depth, problem-solving ability, and the ability to collaborate effectively across teams.

• 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.

Fully remote work opportunity within the United States.

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.

How Jobgether works:

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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