Senior Embedded Engineer/Architect with AI for Edge

Opis stanowiska podany przez pracodawcę:

• Influence the Edge AI strategy by providing expert advice on design and architecture
• Make critical decisions regarding technical directions, scalability, and system performance
• 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
• Collaborate 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
• Work on micro-architecture development, ensuring efficient execution of graphics, compute, and AI workloads within energy and area constraints
• 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 
• Develop project specifications, track progress, and control costs
• Foster a positive work environment, encouraging professional growth and knowledge sharing

Wymagania stawiane pracownikowi:

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

• Experience with edge device hardware and software integration
• Familiarity with edge computing architectures and IoT platforms
• Experience with edge AI applications in domains such as robotics, autonomous vehicles, or industrial automation

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