Director Engineering - Embedded Software

This role is for a hands-on lead specializing in Edge AI deployment. The successful candidate will provide specialized expertise in model optimization at the edge, robust deployment, and MLOps pipeline development. You will leverage your skills in edge optimization, system and embedded knowledge, AI/machine learning, MLOps, computer vision, innovation, and problem solving to drive advanced AI solutions.

We are seeking someone with embedded AI experience, particularly with GPUs or AI accelerators, and strong system solution knowledge for AI application deployment.

You will be part of the Forge and AI team, based in Atlanta, Georgia.

YOU MUST HAVE

• Proven experience in embedded AI with hands-on expertise in GPU or AI accelerator deployment.
• Strong skills in edge model optimization and embedded system architecture.
• Deep understanding of AI/machine learning algorithms and computer vision applications.
• Experience building and managing MLOps pipelines for AI model deployment at the edge.
• Excellent problem-solving skills and a passion for innovation in embedded AI technologies.

WE VALUE

• Background in system solutions with AI application deployment experience.
• Strong knowledge of embedded systems and real-time operating environments.
• Experience working in collaborative, agile environments.
• Advanced degree in Computer Science, Electrical Engineering, or related technical field preferred.

US PERSON REQUIREMENTS:

• Due to compliance with U.S. export control laws and regulations, candidate must be a U.S. Person which is defined as a U.S. citizen, a U.S. permanent resident, or have protected status in the U.S. under asylum or refugee status or have the ability to obtain an export authorization.

KEY RESPONSIBILITIES

• Lead hands-on development and deployment of Edge AI solutions with a focus on model optimization and performance on embedded platforms.
• Design and implement robust MLOps pipelines to support continuous integration and deployment of AI models at the edge.
• Collaborate with cross-functional teams to integrate AI applications into embedded systems using GPUs and AI accelerators.
• Provide technical leadership and mentorship in embedded AI, system architecture, and AI deployment strategies.
• Drive innovation and problem-solving initiatives to enhance AI capabilities and deployment robustness on edge devices.