CUDA Developer

Key Responsibilities :

- Identify and resolve performance issues and bugs related to CUDA usage in various projects

- Analyse and optimize algorithms for improved execution efficiency through parallelization.

- Implement best practices for CUDA programming and stay updated with the latest developments in CUDA and parallel computing technologies.

- Collaborate with cross-functional teams, including product developers, engineers, and other stakeholders, to integrate optimized solutions.

- Monitor and improve system performance, focusing on GPU utilization to ensure maximum computational efficiency.

- Debug and troubleshoot CUDA-related issues in both development and production environments.

- Create detailed performance reports and optimization plans to communicate improvements effectively with team members and management.

- Contribute to code reviews, ensuring the code is optimized, maintainable, and adheres to CUDA best practices.

- Continuously enhance your skills in CUDA and parallel computing through training, research, and Qualifications :

- Bachelor's degree in Computer Science, Computer Engineering, or a related field.

- Minimum of 5 years of experience in software development, with at least 2 years dedicated to CUDA development.

- Strong problem-solving skills, with the ability to identify bottlenecks and implement effective solutions for performance improvement.

- Proven ability to work both independently and within a collaborative team environment.

- Experience in analysing and optimizing GPU workloads, particularly for high-performance applications.

- Solid understanding of the principles of parallel computing and the ability to apply them to practical development scenarios.

- Good communication skills, enabling clear articulation of technical challenges and solutions in both written and verbal formats.

Required Skills :

Proficiency in C/C++ :

- Strong command of C/C++ programming languages, crucial for implementing high-performance CUDA applications.

CUDA Experience :

- Extensive experience working with CUDA, particularly versions 12.0 or higher (12.3 preferred).

- You should have a deep understanding of how to use CUDA for optimizing computational tasks on GPUs.

Performance Optimization :

- Skilled in identifying performance bottlenecks and optimizing code to ensure efficient GPU utilization and faster execution.

Parallel Computing :

- In-depth knowledge of parallel programming principles and the ability to design and implement efficient parallel algorithms.

cuBLAS and cuDNN Libraries :

- Familiarity with CUDA libraries such as cuBLAS (Basic Linear Algebra Subprograms) and cuDNN (Deep Neural Network library), which are crucial for GPU-accelerated operations.

Code Optimization :

- Expertise in fine-tuning code for resource efficiency and improved performance, especially for high-performance computing tasks.

Debugging and Troubleshooting :

- Strong debugging skills to identify and resolve issues in CUDA applications, ensuring smooth operation in development and production Skills :

- Ability to work closely with other engineers and teams, sharing knowledge and collaborating on performance improvements and system enhancements.