Machine Learning Engineer — Pre-training (LLM)

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.

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