Job description

About the Institute of Foundation Models We are a dedicated research lab for building, understanding, using, and risk-managing foundation models. Our mandate is to advance research, nurture the next generation of AI builders, and drive transformative contributions to a knowledge-driven economy. As part of our team, you’ll have the opportunity to work on the core of cutting-edge foundation model training, alongside world-class researchers, data scientists, and engineers, tackling the most fundamental and impactful challenges in AI development. You will participate in the development of groundbreaking AI solutions that have the potential to reshape entire industries. Strategic and innovative problem-solving skills will be instrumental in establishing MBZUAI as a global hub for high-performance computing in deep learning, driving impactful discoveries that inspire the next generation of AI pioneers. The Role The GPU Kernel Engineer will play a role at the forefront of optimizing performance for the machine learning software stacks, especially at training and inference, and support the team to develop new and cutting-edge systems. The ideal candidate will have a strong background in parallel computing, and hands-on experience in system level coding, debug methodologies, and large-scale machine learning experience. This role focuses on CUDA kernel development and optimization. Distributed training experience is a plus. Key Responsibilities Understand, analyze, profile, optimize, and provide guidance to the team on deep learning workloads on state-of-the-art hardware and software platforms to improve their efficiency with different levels of optimization Design and implement performance benchmarks and testing methodologies to evaluate application performance Build tools to automate workload analysis, workload optimization, and other critical workflows Triage system issues and identify bottleneck and inefficiencies by analyzing the sources of issues and the impact on hardware, network and propose solutions to enhance GPU utilization Support the team to develop appropriate kernels and systems for new model architectures and algorithms Participate in, or lead design reviews with peers and stakeholders to decide amongst available technologies. Review code developed by other developers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency). Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback. Represent MBZUAI at industry conferences and events, showcasing the institution’s cutting-edge HPC and deep learning capabilities and establishing MBZUAI as a global leader in AI research and innovation. Perform all other duties as reasonably directed by the line manager that are commensurate with these functional objectives. Validate CUDA kernel outputs and gradients against reference implementations, and benchmark representative shapes, dtypes, and model workloads. Technical Qualifications Must-Haves: Strong C++ skills and hands-on CUDA kernel development and optimization for deep-learning workloads. Understanding of GPU memory hierarchy, warp/block execution, and compute-memory trade-offs, with demonstrated profiling-driven optimization. Strong Python skills and experience integrating kernels with PyTorch or an equivalent framework, including numerical and gradient validation where needed. Nice-to-Haves: Experience with Triton, CUTLASS, or PTX/SASS analysis. Experience with multi-node distributed training or inference systems. Experience validating mixed-precision computations, such as BF16 or FP8. Benefits Include *Comprehensive medical, dental, and vision benefits *Bonus *401K Plan *Generous paid time off, sick leave and holidays *Paid Parental Leave *Employee Assistance Program *Life insurance and disability

Requirements

Must-haves

About the Institute of Foundation Models
Understand, analyze, profile, optimize, and provide guidance to the team on deep learning workloads on state-of-the-art hardware and software platforms to improve their efficiency with different levels of optimization
Design and implement performance benchmarks and testing methodologies to evaluate application performance
Build tools to automate workload analysis, workload optimization, and other critical workflows
Triage system issues and identify bottleneck and inefficiencies by analyzing the sources of issues and the impact on hardware, network and propose solutions to enhance GPU utilization
Support the team to develop appropriate kernels and systems for new model architectures and algorithms
Participate in, or lead design reviews with peers and stakeholders to decide amongst available technologies.
Review code developed by other developers and provide feedback to ensure best practices (e.g., style guidelines, checking code in, accuracy, testability, and efficiency).
Contribute to existing documentation or educational content and adapt content based on product/program updates and user feedback.
Represent MBZUAI at industry conferences and events, showcasing the institution’s cutting-edge HPC and deep learning capabilities and establishing MBZUAI as a global leader in AI research and innovation.
Perform all other duties as reasonably directed by the line manager that are commensurate with these functional objectives.
Validate CUDA kernel outputs and gradients against reference implementations, and benchmark representative shapes, dtypes, and model workloads.
Technical Qualifications
Strong C++ skills and hands-on CUDA kernel development and optimization for deep-learning workloads.
Understanding of GPU memory hierarchy, warp/block execution, and compute-memory trade-offs, with demonstrated profiling-driven optimization.
Strong Python skills and experience integrating kernels with PyTorch or an equivalent framework, including numerical and gradient validation where needed.

Nice-to-haves

This role focuses on CUDA kernel development and optimization. Distributed training experience is a plus.
Experience with Triton, CUTLASS, or PTX/SASS analysis.
Experience with multi-node distributed training or inference systems.
Experience validating mixed-precision computations, such as BF16 or FP8.
Benefits Include

Application prep

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Keywords to mirror

aimachine learningpython

Platform: lever
Effort: quick

Benefits

Benefits detected in the listing.

Dental

Vision

401k

Paid Time Off

Parental Leave