CUDA with C++ and Python Developer

Skill

5+ years of professional software development experience with strong focus on CUDA development.
Strong proficiency in C/C++.
Strong hands-on experience with Python, especially in scientific computing using PyTorch and NumPy.
Experience working with CUDA version 12.3 or above.
Strong understanding of GPU programming concepts, parallel computing, and performance optimization.

Availability

8 hours per day

Role Overview

We are looking for experienced CUDA Developers to work on cutting-edge AI and machine learning projects. In this role, you will contribute to improving large language model (LLM) capabilities by solving complex coding problems, optimizing GPU-based workloads, reviewing model-generated code, and helping train AI systems to produce high-quality CUDA and parallel computing solutions.

The ideal candidate should have strong expertise in CUDA, GPU programming, parallel computing, performance optimization, and Python-based machine learning ecosystems.

What does day-to-day look like:

Solve advanced CUDA and parallel computing problems involving GPU acceleration and performance optimization.
Review, evaluate, and improve AI-generated CUDA/C++/Python code.
Analyze GPU kernel performance and optimize algorithms for throughput, latency, and memory efficiency.
Work with CUDA libraries and frameworks such as Thrust, cuBLAS, and cuDNN.
Develop high-quality prompts, solutions, explanations, and evaluations to improve AI model reasoning and coding performance.
Debug and resolve issues related to CUDA kernels, memory management, synchronization, and resource utilization.
Collaborate with cross-functional teams working on AI model training and evaluation.
Stay updated with the latest developments in CUDA, GPU architectures, and parallel computing best practices.

Requirements:

Bachelor's degree in Computer Science, Computer Engineering, or a related technical field.
5+ years of professional software development experience with strong focus on CUDA development.
Strong proficiency in C/C++.
Strong hands-on experience with Python, especially in scientific computing using PyTorch and NumPy.
Experience working with CUDA version 12.3 or above.
Strong understanding of GPU programming concepts, parallel computing, and performance optimization.
Experience optimizing code for efficient resource utilization and high-performance execution.
Familiarity with CUDA frameworks and libraries such as Thrust, cuBLAS, and cuDNN.
Ability to solve complex technical problems independently.
Strong written and verbal communication skills.
Prior experience contributing to AI/ML systems or LLM-related projects is a plus.

Evaluation Process (approximately 60 mins):

One round of Technical interviews