Researcher - AI Computing System
- Vancouver, BC, Canada
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Original job description
Responsibilities
- Aiming at key industry AI application scenarios such as large model training and inference, this role focuses on advancing performance, efficiency, and usability of AI systems on the Ascend platform. The work involves low-precision training, multimodal optimization, reinforcement learning, and training resource optimization to address system bottlenecks and deliver next-generation AI capabilities
- Responsible for design and development of optimization solutions for AI training and inference systems, with a focus on FP8 optimization, RL-driven training agents, multimodal reinforcement learning or next-generation multi-modal understanding & generation
- Combine AI algorithm requirements with system-level architectural optimization in computing, I/O, scheduling, and precision control to improve performance
- Build stable, efficient AI training clusters, leveraging dynamic cluster configuration and precision control to ensure scalability and reliability
- Develop software frameworks, operator libraries, acceleration libraries, and system-level optimizations for NPU platforms to accelerate large-model AI training
- Drive innovation in optimizing large-model training and inference with low-precision training, parallel strategy tuning, and reinforcement learning
- Grasp the latest research progress and technological trends in AI computing cluster architecture design, training acceleration, and inference acceleration across academia and industry to strengthen the competitiveness of AI computing cluster systems
More about this job
Responsibilities
Design and develop system-level optimization solutions for AI training and inference on Huawei's Ascend platform, including low-precision training, reinforcement learning, multimodal workloads, and large-model acceleration. Improve computing architecture, training clusters, software frameworks, and acceleration libraries to increase performance, efficiency, scalability, and reliability.
Requirements
The role calls for expertise in AI training and inference optimization, computing systems, and large-model acceleration, including low-precision methods, reinforcement learning, and multimodal workloads. It also requires the ability to combine AI algorithm needs with system-level optimization and stay current with research in cluster architecture and training and inference acceleration.
Skills
- AI Training Optimization
- AI Inference Optimization
- FP8 Optimization
- Low-Precision Training
- Reinforcement Learning
- Multimodal Optimization
- AI Computing Architecture
- Computing Systems Optimization
- Training Cluster Optimization
- Parallel Strategy Tuning
- NPU Platforms
- Operator Libraries
- Acceleration Libraries
- Cluster Configuration
- Precision Control
- Large-Model Acceleration
Visa sponsorship
Not detected in the job text
Categories
- Technology
- Software
- Engineering
- Science & Research
- Data & Analytics
Keywords
- Huawei Ascend
- AI Computing Systems
- AI Training
- AI Inference
- FP8
- Low-Precision Training
- Multimodal Optimization
- Multimodal Reinforcement Learning
- Reinforcement Learning
- Large-Model Training
- Large-Model Inference
- AI Computing Architecture
- Computing Optimization
- I/O Optimization
- Scheduling
- Precision Control
- Training Clusters
- Dynamic Cluster Configuration
- NPU Platforms
- Software Frameworks
- Operator Libraries
- Acceleration Libraries
- Parallel Strategy Tuning
- Training Acceleration
- Inference Acceleration
- Cluster Architecture