PyCUDA Engineer
Role Description The NVIDIA AI Engineer - PyCUDA role is a remote contract position focused on building and optimizing GPU-accelerated AI solutions . Day-to-day responsibilities include designing and implementing CUDA/PyCUDA-based algorithms, optimizing neural network models for NVIDIA GPUs, and integrating GPU-accelerated components into production software systems. The engineer will collaborate with data scientists and software developers to deploy scalable AI pipelines for tasks such as pattern recognition and natural language processing, ensuring performance, reliability, and maintainabil…
- Remote
- ["Canada"]
- Posted Jul 8, 2026
- 1 position
Job summary
Role Description The NVIDIA AI Engineer - PyCUDA role is a remote contract position focused on building and optimizing GPU-accelerated AI solutions . Day-to-day responsibilities include designing and implementing CUDA/PyCUDA-based algorithms, optimizing neural network models for NVIDIA GPUs, and integrating GPU-accelerated components into production software systems. The engineer will collaborate with data scientists and software developers to deploy scalable AI pipelines for tasks such as pattern recognition and natural language processing, ensuring performance, reliability, and maintainability. Additional responsibilities include profiling and debugging GPU code, contributing to system architecture decisions, and documenting technical designs and best practices for the team. Qualifications Strong foundation in Computer Science and Software Development, including proficiency in Python and experience with PyCUDA or CUDA programming. Hands-on experience with Neural Networks and Pattern Recognition, especially in designing and optimizing deep learning models on NVIDIA GPU architectures. Knowledge of Natural Language Processing (NLP) techniques and frameworks, with experience applying them in real-world AI applications. Solid understanding of parallel computing concepts, GPU memory management, and performance profiling tools (e.g., NVIDIA Nsight, CUDA profiler). Experience with modern machine learning frameworks (e.g., PyTorch, TensorFlow) and integrating GPU-accelerated components into production systems. Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or a related technical field, or equivalent professional experience. Ability to work independently in a remote, contract setting, collaborate effectively with cross-functional teams, and communicate complex technical concepts clearly. Experience with MLOps practices, cloud platforms (e.g., AWS, GCP, Azure), and interest in AI for talent management is an asset.
What you’ll do
The engineer will design and implement CUDA/PyCUDA-based algorithms and optimize neural network models for NVIDIA GPUs. They will also collaborate with data scientists and software developers to deploy scalable AI pipelines.
Requirements
Candidates should have a strong foundation in Computer Science and Software Development, with proficiency in Python and experience in PyCUDA or CUDA programming. A Bachelor’s or Master’s degree in a related field or equivalent professional experience is required.
Other relevant skills
Identified from the job description. Confirm important requirements above.
- Python
- PyCUDA
- CUDA
- Neural Networks
- Pattern Recognition
- Natural Language Processing
- Deep Learning
- Parallel Computing
- GPU Memory Management
- Performance Profiling
- Machine Learning
- PyTorch
- TensorFlow
- MLOps
- Cloud Platforms
- AI
Additional details
- Minimum education
- Bachelor’s degree
- Minimum experience
- 2+ years