Distributed Machine Learning Engineer

On-siteSeniorML Engineerposted 18mo ago

✦Role summaryAI-generated

You will design and implement distributed training pipelines for foundation models, leveraging high-performance computing resources to accelerate deep learning workloads. The role involves collaborating with researchers and engineers to optimize GPU utilization and ensure scalable, fault-tolerant model training across large clusters.

Skills required

Distributed training Deep learning High-performance computing Foundation models GPU PyTorch TensorFlow AI Machine learning

About this role

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 Distributed ML 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.

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.