- Location
- Raleigh, NC,US, US · Santa Clara, CA,US, US · San Diego, CA,US, US
- Workplace
- Remote
- Type
- Full-time
- Department
- Engineering
- Seniority
- Senior
- Education
- PhD
- Source
- Eightfold
Description
Understand trends in ML network design through customer engagements and latest academic research and determine how this will affect both SW and HW design Work with customers to determine hardware requirements for AI training systems Analysis of current accelerator and GPU architectures Architect enhancements required for efficient training of AI models Design and architecture of: Perform analysis of performance/area/power trade-offs for future HW and SW ML algorithms including impact of SOC components (memory and bus impacts) Ability to perform Competitive Analysis Master's degree in Computer Science, Engineering, Information Systems, or related field 3+ years Hardware Engineering experience defining architecture of GPUs or accelerators used for training of AI models In-depth knowledge of nVidia/AMD GPU capabilities and architectures Knowledge of LLM architectures and their HW requirements Knowledge of computer architecture, digital circuits and hardware simulators Knowledge of communication protocols used in AI systems Knowledge of Network-on-Chip (NoC) designs used in System-on-Chip (SoC) designs Understanding of various memory technologies used in AI systems Experience in modeling hardware and workloads in order to extract performance and power estimates High-level hardware modeling experience preferred Knowledge of AI Training systems such as NVIDIA DGX and NVL72 Experience training and finetuning LLMs using distributed training framework such as DeepSpeed, FSDP Knowledge of front-end ML frameworks (i.e.,TensorFlow, PyTorch) used for training of ML models Strong communication skills (written and verbal) Detail-oriented with strong problem-solving, analytical and debugging skills Demonstrated ability to learn, think and adapt in a fast-changing environment Ability to code in C++ and Python Knowledge of a variety of classes of ML models (i.e. CNN, RNN, etc) Bachelor's degree in Computer Science, Engineering, Information Systems, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. OR Master's degree in Computer Science, Engineering, Information Systems, or related field and 1+ year of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience. OR PhD in Computer Science, Engineering, Information Systems, or related field.