The Role
Design, extend, and maintain distributed training infrastructure: modify frameworks (DeepSpeed, FSDP, etc.), implement distributed optimizers, build multi-node launch/config systems, implement experiment tracking and monitoring, optimize reliability and performance, and write production-quality ML infra code in PyTorch or JAX.
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
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
Skills Required
- 5+ years of experience in ML systems, infrastructure, or distributed training
- Experience modifying distributed ML frameworks (DeepSpeed, FSDP, FairScale, Horovod)
- Strong software engineering fundamentals (Python, systems design, testing)
- Proven multi-node experience and debugging skills (Slurm, Kubernetes, Ray; NCCL/GLOO)
- Ability to implement algorithms across GPUs/nodes from mathematical specifications
- Experience working on ML platform/infrastructure or distributed inference optimization
- Experience with large-scale machine learning workloads and strong ML fundamentals
- Exposure to mixed-precision training (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 and building custom training pipelines at scale
- Deep familiarity with training infrastructure and performance tuning
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The Company
What We Do
First a passion, then an idea transformed into success – when it comes to pioneering automation and digitalisation technology, the ifm group is the ideal partner. Since its foundation in 1969, ifm has developed, produced and sold sensors, controllers, software and systems for industrial automation and for SAP-based solutions for supply chain management and shop floor integration worldwide. As one of the pioneers of Industry 4.0, ifm develops and implements consistent solutions to digitalise the entire value chain “from sensor to ERP”. Today, the second-generation family-run ifm group has more than 8,750 employees and is one of the worldwide market leaders. The group combines the internationality and innovative strength of a growing group of companies with the flexibility and close customer contact of a medium-sized company.
