Machine Learning Engineer — Pre-training (LLM)

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.

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.

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)

Experience with large-scale pre-training

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 building custom training pipelines at scale and modifying them for custom needs

Deep familiarity with training infrastructure and performance tuning

Compensation

$150,000 – $450,000/year + discretionary performance bonus + benefits

The posted salary range represents the company’s good faith estimate of the base compensation for this position upon hire. The actual base compensation offered may vary within this range depending on individual qualifications, including but not limited to relevant skills, experience, education, certifications, and specific business needs. In addition to base salary, total compensation includes an annual discretionary performance bonus and comprehensive benefits.

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