Senior Parallel Computing Engineer

1 week ago


Mumbai, Maharashtra, India Kudzu Infotech Full time

Tech Requirements :

1. We are seeking a skilled Senior Parallel Computing Engineer to join our team at Kudzu Infotech. The ideal candidate will have ~5 years of experience in software development with a strong emphasis on simulation, physics engines, or related areas.

2. Solid understanding of 3D graphics, real-time rendering, and simulation techniques is a must.

3. Proficiency in C++, Python, and/or other relevant programming languages is expected.

4. Experience with technologies, such as CUDA, Pytorch, DGL, PYG, cuGraph, and related GPU programming frameworks is highly valued.

5. Familiarity with Agile development methodologies is a plus.

6. Prior experience in the development of virtual reality (VR) or augmented reality (AR) applications is desired.

7. Knowledge of cloud computing technologies and distributed systems is necessary.

Key Responsibilities :

- Design and implement parallel algorithms and data structures using CUDA C++ programming language to leverage the computational power of GPUs for accelerating scientific simulations, numerical computations, and machine learning algorithms.

- Develop CUDA kernels, device functions, and memory management techniques to optimize performance, throughput, and scalability of GPU-accelerated applications across diverse domains and use cases.

- Collaborate with software architects, researchers, and domain experts to understand project requirements, design specifications, and performance objectives for CUDA-based applications and algorithms.

- Analyze and optimize code performance, memory utilization, and parallel scalability using profiling tools, debuggers, and performance analysis techniques to identify bottlenecks and opportunities for improvement.

- Implement software solutions for parallel computing tasks, including matrix operations, linear algebra routines, signal processing algorithms, image processing filters, and deep learning models using CUDA libraries and frameworks.

- Integrate CUDA-accelerated components into larger software systems, frameworks, or platforms, ensuring compatibility, interoperability, and performance consistency across heterogeneous computing environments.

- Collaborate with hardware engineers, system architects, and platform developers to optimize software-hardware interactions, exploit GPU features, and maximize utilization of GPU resources for compute-intensive tasks.

- Write clean, modular, and maintainable code using software development best practices, version control systems, and coding standards, documenting design decisions, implementation details, and usage guidelines.



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