The Role
Optimize and benchmark deep learning training and inference stacks on HPC/GPU hardware; profile and triage system bottlenecks; build automation tools; develop kernels and systems for new models; lead design reviews, review code, document work, and represent the lab at conferences.
Summary Generated by Built In
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.
Academic Qualifications
- Ph.D. in CS, EE or CSEE with 1+ years working experience, OR
- Masters in CS, EE or CSEE or equivalent experience with 2+ year working experience
Visa Sponsorship
This position is eligible for visa sponsorship.
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
Skills Required
- Ph.D. in CS, EE, or CSEE with 1+ years experience OR Master’s in CS, EE, or CSEE with 2+ years experience
- Strong background in parallel computing
- Hands-on experience in system-level coding and debugging methodologies
- Large-scale machine learning experience (training and inference stacks)
- Experience analyzing, profiling, and optimizing deep learning workloads on modern hardware and software platforms
- Ability to design and implement performance benchmarks and testing methodologies
- Experience building tools to automate workload analysis and optimization workflows
- Experience triaging system issues, identifying bottlenecks across hardware, network, and software to improve GPU utilization
- Experience supporting development of kernels and systems for new model architectures and algorithms
- Experience participating in or leading design reviews and performing code reviews to ensure best practices
- Ability to contribute to documentation and educational content
Am I A Good Fit?
Get Personalized Job Insights.
Our AI-powered fit analysis compares your resume with a job listing so you know if your skills & experience align.
Success! Refresh the page to see how your skills align with this role.
The Company
