Research Engineer - Performance Optimization

About the Role

Key Responsibilities

• Ensure efficient implementation of models & systems for data processing, training, inference and deployment.
• Identify and implement optimization techniques for massively parallel and distributed systems.
• Identify and remedy efficiency bottlenecks (memory, speed, utilization) by profiling and implementing high-performance CUDA, Triton, C++ and PyTorch code.
• Work closely together with the research team to ensure systems are planned to be as efficient as possible from start to finish.
• Build tools to visualize, evaluate and filter datasets.
• Implement cutting-edge product prototypes based on multimodal generative AI.

Requirements

• Experience training large models using Python & PyTorch, including practical experience working with the entire development pipeline from data processing, preparation & data loading to training and inference.
• Experience optimizing and deploying inference workloads for throughput and latency across the stack (inputs, model inference, outputs, parallel processing etc.).
• Experience with profiling CPU & GPU code in PyTorch, including Nvidia Nsight or similar.
• Experience writing & improving highly parallel & distributed PyTorch code, with familiarity in DDP, FSDP, Tensor Parallel, etc.
• Experience writing high-performance parallel C++.
• Bonus if done within an ML context with PyTorch, like for data loading, data processing, inference code.
• Experience with high-performance Triton / CUDA and writing custom PyTorch kernels.
• Top candidates will be able to utilize tensor cores; optimize performance with CUDA memory and other similar skills.
• Good to have experience working with Deep learning concepts such as Transformers & Multimodal Generative models such as Diffusion Models and GANs.
• Good to have experience building inference / demo prototype code (incl. Gradio, Docker etc.).

Nice to Have

• Experience with building inference / demo prototype code (incl. Gradio, Docker etc.)
• Experience with Deep learning concepts such as Transformers & Multimodal Generative models such as Diffusion Models and GANs.

Qualifications

• Experience training large models using Python & PyTorch, including practical experience working with the entire development pipeline from data processing, preparation & data loading to training and inference.

Benefits & Perks

• The pay offered to a successful candidate will be determined by various factors, including education, work experience, location, job responsibilities, certifications, and more.
• IntelliPro provides a comprehensive benefits package, all subject to eligibility.