Machine Learning Engineer — Pre-training (LLM)
This engineer extends and scales distributed LLM pre-training infrastructure at a foundation-model research lab: modifying frameworks like DeepSpeed/FSDP, implementing distributed optimizers from math specs, and building launch, monitoring, and checkpointing systems across multi-node GPU clusters using PyTorch or JAX.
- Compensation
- $150K–$450K / yr
- Location
- Sunnyvale, CA
- Work setup
- Onsite
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 5+ years
- Category
- Ml / AI
- Posted
Skills
Must have
- 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)
- Experience with large-scale pre-training
Nice to have
- 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 building custom training pipelines at scale and modifying them for custom needs
- Deep familiarity with training infrastructure and performance tuning
Full description
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
Much of the work will support large-scale pre-training, and pre-training experience is 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. Select and validate data, tensor, pipeline, expert, and context parallelism strategies as appropriate for the model and cluster.
- 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.
- Data Loading & Checkpointing – Build and maintain data-loading and checkpoint/restart workflows, restoring model, optimizer, RNG, and data progress after interruptions.