Machine Learning Infrastructure Engineer

AI in this role

pytorchjax

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 We're looking for a distributed ML infrastructure engineer to help extend and scale our training systems. You’ll work side-by-side with world-class researchers and engineers to: • Extend distributed training frameworks (e.g., DeepSpeed, FSDP, FairScale, Horovod) • Implement distributed optimizers from mathematical specs • Build robust config + launch systems across multi-node, multi-GPU clusters • Own experiment tracking, metrics logging, and job monitoring for external visibility • Improve training system reliability, maintainability, and performance • While much of the work will support large-scale pre-training, pre-training experience is not required. Strong infrastructure and systems experience is what we value most. Key Responsibilities • Distributed Framework Ownership – Extend or modify training frameworks (e.g., DeepSpeed, FSDP) to support new use cases and architectures. • Optimizer Implementation – Translate mathematical optimizer specs into distributed implementations. • Launch Config & Debugging – Create and debug multi-node launch scripts with flexible batch sizes, parallelism strategies, and hardware targets. • Metrics & Monitoring – Build systems for experiment tracking, job monitoring, and logging usable by collaborators and researchers. • Infra Engineering – Write production-quality code and tests for ML infra in PyTorch or JAX; ensure reliability and maintainability at scale. Qualifications Must-Haves: • 5+ years of experience in ML systems, infra, or distributed training • Experience modifying distributed ML frameworks (e.g., DeepSpeed, FSDP, FairScale, Horovod) • Strong software engineering fundamentals (Python, systems design, testing) • Proven multi-node experience (e.g., Slurm, Kubernetes, Ray) and debugging skills (e.g., NCCL/GLOO) • Ability to implement algorithms across GPUs/nodes based on mathematical specs • Experience working on an ML platform/ infrastructure, and/or distributed inference optimization team • Experience with large-scale machine learning workloads (strong ML fundamentals) Nice-to-Haves: • Exposure to mixed-precision training (e.g., bf16, fp8) with accuracy validation • Familiarity with performance profiling, kernel fusion, or memory optimization • Open-source contributions or published research (MLSys, ICML, NeurIPS) • CUDA or Triton kernel experience • Experience with large-scale pre-training • Experience building custom training pipelines at scale and modifying them for custom needs • Deep familiarity with training infrastructure and performance tuning

How we rate this

Machine Learning Infrastructure Engineer at IFM rates 100 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

PyTorchJax

Questions you could be asked

What's a project where you used PyTorch hands-on?
Walk me through how you've used Jax in your day-to-day work.
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: PyTorch and Jax. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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