Machine Learning Engineer — Pre-training (LLM)

Lever, Inc.; Sunnyvale (CA); In loco; USD 150.000 - 450.000; Tempo pieno

Vantaggi offerti da questo lavoro

Medical, dental, and vision benefits

Bonus

401K Plan

Generous paid time off

Parental Leave

Employee Assistance Program

Life insurance and disability

Descrizione del lavoro

The Institute of Foundation Models is seeking a distributed ML infrastructure engineer to extend and scale our training systems. You’ll work with world‑class researchers to extend distributed training frameworks (DeepSpeed, FSDP, FairScale, Horovod) and build robust multi‑node launch scripts.

You will own experiment tracking, metrics logging, and job monitoring for external visibility, and aim to improve reliability and performance of large‑scale pre‑training pipelines.

Competenze

• 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 (Slurm, Kubernetes, Ray) and debugging skills (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 and pre-training.

Mansioni

• 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.
• Data Loading & Checkpointing – Build and maintain data-loading and checkpoint/restart workflows, restoring model, optimizer, RNG, and data progress after interruptions.

Conoscenze

ML systems

Distributed training

Python

Slurm

Kubernetes

Ray

NCCL/GLOO

Strumenti

DeepSpeed

FSDP

FairScale

Horovod

Descrizione del lavoro

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 forhigh-performance computing in deep learning, driving impactful discoveries that inspire the next generation of AIpioneers.

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.

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

$150,000 - $450,000 a year

Salary Range

The posted salary range represents the company’s good faith estimate of the compensation for this position upon hire. The actual compensation offered may vary within this range depending on individual qualifications, including but not limited to relevant skills, experience, education, certifications, geographic location, and specific business needs.

The posted salary range represents the company’s good faith estimate of the compensation for this position upon hire. The actual compensation offered may vary within this range depending on individual qualifications, including but not limited to relevant skills, experience, education, certifications, geographic location, and specific business needs.

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