Remote Edge AI Staff Engineer (9745)

Description:

• We are seeking a talented Edge AI Staff Engineer with specialized expertise in GPU/TPU acceleration to join our team.
• The ideal candidate will have extensive hands-on experience in local Large Language Models (LLM) inference with embedded GPU/TPU architectures.
• As Staff Engineer specializing in Edge AI, you will play a crucial role in shaping the future Edge AI solution, leveraging the power of GPU/TPU acceleration and enterprise-grade, large-scale edge compute.
• The successful candidate will combine technical excellence with effective leadership, creating a positive impact on both projects and team dynamics.
• Key responsibilities include influencing the Edge AI strategy by providing expert advice on design and architecture, making critical decisions regarding technical directions, scalability, and system performance.
• You will develop and optimize AI inference models for deployment on edge devices with embedded GPU/TPU accelerators, focusing on local Low Latency Model (LLM) inference.
• Implement and fine-tune low-latency model inference pipelines to meet real-time performance requirements.
• Collaborate with cross-functional teams to integrate AI inference solutions into edge computing platforms and applications.
• Work with the GPU Hardware Design Team to design and optimize GPUs that power next-generation devices.
• Conduct performance profiling and optimization to maximize the efficiency of GPU/TPU acceleration for local LLM inference.
• Stay current with advancements in GPU/TPU technologies and edge AI frameworks, incorporating them into solution designs as appropriate.
• Provide technical expertise and support to project teams, ensuring successful implementation and deployment of edge AI solutions.
• Lead and inspire a team of engineers, providing guidance, setting goals, and ensuring collaboration.
• Oversee project planning, execution, and delivery, ensuring alignment with business objectives.
• Manage all phases of technical projects, from conception to completion, developing project specifications, tracking progress, and controlling costs.
• Foster a positive work environment, encouraging professional growth and knowledge sharing.

Requirements:

• A Bachelor’s degree in computer science, Engineering, or a related field is required; a Master’s degree is preferred.
• A minimum of 5 years of hands-on experience in AI model development and deployment, with a focus on edge computing and local LLM inference is required.
• Strong programming skills in languages such as Python and C++ are necessary.
• Proficiency in LLM frameworks (e.g., vLLM, Text generation inference, OpenLLM, Ray Serve, and HuggingFace Transformers) and deep learning libraries is required.
• Extensive experience with GPU/TPU acceleration for AI inference, including optimization techniques (tensor, pipeline, data, sharded data parallelism) and performance tuning is essential.
• Hands-on experience with one or more GPU frameworks: CUDA, Vulkan, OpenCL is required.
• Deep knowledge of GPU memory layout and familiarity with NVIDIA Jatison, ARM Mali, or relevant SoC configurations is necessary.
• Knowledge of parallel computation, memory scheduling, and structural optimization is required.
• Excellent problem-solving and analytical skills, with a passion for innovation and continuous learning are essential.

Benefits:

• Join a company that values inclusion and fosters an atmosphere where all employees thrive because of their differences.
• Be part of a global networking leader that is well-positioned to deliver scalable outcomes and accelerate digital transformation efforts.
• Work in a positive environment that encourages professional growth and knowledge sharing.
• Opportunity to influence the Edge AI strategy and work on cutting-edge technologies in GPU/TPU acceleration.
• Engage in a role that combines technical excellence with effective leadership, impacting both projects and team dynamics.