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# Machine Learning Engineer -- Pre-training (LLM)

- **Company:** Institute Inc
- **Location:** Sunnyvale, CA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Computer Clusters, Software Code Optimization, Profiling, Nvidia CUDA, Extract Transform Load (ETL), Software Debugging, Distributed Computing Environment, Python (Programming Language), Machine Learning, Node.Js, Performance Tuning, Tensorflow, Azure Machine Learning, Data Logging, Graphics Processing Unit (GPU), Pytorch, DeepSpeed, Large Language Models, Reliability of Systems, Kubernetes, Production Code, Free and Open-Source Software, Build Tools, Slurm, Machine Learning Operations
- **Published:** October 11, 2026
- **Apply:** https://www.juju.com/job/16_f188627d

## About the Role

* 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

## Description

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.