Edge AI Engineer

Accountabilities: The Edge AI Engineer will be responsible for designing, optimizing, and deploying machine learning models that operate efficiently on edge devices. This role requires a balance of AI development expertise and systems-level engineering skills to create reliable solutions under real-world hardware and connectivity constraints.

Design, optimize, and deploy machine learning models for mobile platforms, embedded systems, and specialized edge accelerators.

Apply model compression, quantization, pruning, and other optimization techniques to improve AI performance and efficiency.

Develop production-ready edge AI solutions using Python, C++, and relevant machine learning frameworks.

Analyze and optimize model performance through profiling, benchmarking, and hardware-aware engineering practices.

Deploy and maintain machine learning models across mobile and embedded environments.

Work with hardware architectures and edge computing constraints to make effective engineering trade-offs.

Implement solutions that address on-device privacy, security, and reliability requirements.

Collaborate with product, software, hardware, and research teams to deliver scalable AI capabilities.

Contribute to improvements in edge AI development practices, tools, and deployment workflows.

Requirements:

The ideal candidate brings strong machine learning engineering experience with a proven ability to build and deploy AI solutions outside traditional data center environments. They should combine technical depth, problem-solving skills, and the ability to collaborate effectively across engineering teams.

Bachelor’s or Master’s degree in Computer Science, Computer Engineering, or a related technical field.

6+ years of experience in machine learning engineering, including significant experience with edge or mobile AI applications.

Strong programming skills in Python and C++.

Hands-on experience with model compression, quantization, pruning, and optimization techniques.

Experience working with at least one major edge inference framework.

Strong understanding of mobile and embedded hardware architectures.

Proven experience deploying machine learning models into production environments.

Strong performance engineering, profiling, and troubleshooting skills.

Knowledge of on-device privacy and security considerations.

Excellent communication skills with the ability to collaborate across technical and business teams.

Experience with custom NPU or DSP toolchains is preferred.

Familiarity with federated learning, on-device personalization, or safety-critical edge deployments is a plus.

Experience optimizing large language models for on-device inference is highly desirable.

Benefits:

Competitive annual salary range of $100,000–$150,000 .

Fully remote position within the United States.

Full-time direct employment opportunity.

Opportunity to work on cutting-edge AI and machine learning technologies.

Career growth opportunities within an established technology organization.

Collaborative environment focused on innovation and advanced engineering 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? 

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