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Together, we advance your career.
GPU Kernel Optimization Engineer
Key Responsibilities
- GPU Kernel Optimization: Develop and optimize low-level GPU kernels to accelerate inference and training of large machine learning models. Maximize computational efficiency and reduce execution time while ensuring model accuracy.
- Multi-GPU and Multi-Node Optimization:



Design and implement strategies for distributed model training and inference across multiple GPUs and nodes. Address data parallelism and model parallelism challenges to fully utilize available resources.
- Performance Profiling: Profile and analyze system and application performance to identify bottlenecks and areas for improvement. Use profiling tools to understand and optimize hardware resource utilization.
- Parallel Computing: Leverage parallel computing techniques to improve the scalability and performance of machine learning workloads. Implement multi-threading and GPU synchronization techniques.
- Benchmarking and Testing: Develop benchmarks and testing procedures to assess the performance and stability of optimized models and frameworks. Ensure that the solutions meet or exceed the defined performance criteria.
- Documentation: Create detailed documentation of optimizations, best practices, and implementation guideline

📌 Staff/Principal GPU/CPU Kernel Optimization Engineer (Hyderabad)
🏢 AMD
📍 Hyderabad