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Machine Learning Infrastructure Engineer
- Sunnyvale, California, United States
- Sunnyvale, California, United States
Über
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
$150,000 - $450,000 a year
Benefits
Comprehensive medical, dental, and vision
401(k) program
Generous PTO, sick leave, and holidays
Paid parental leave and family‑friendly benefits
On‑site amenities and perks: Complimentary lunch, gym access, and a short walk to the Sunnyvale Caltrain station
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Sprachkenntnisse
- English
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