ML Infra Engineer - Distributed Training & Orchestration

MaxIT Consulting - Max Corporate Group; San Francisco (CA); Hybrid; USD 150,000 - 190,000; Full time

Job summary

MaxIT Consulting – Max Corporate Group is seeking a Research Engineer to design and operate the training and environment infrastructure for a fast-growing AI company in San Francisco. You will own long-horizon evaluation environments, post-training infrastructure, and scalable systems used to train and deploy enterprise models.

The role sits on a small founding-stage team, delivering end-to-end infrastructure for research and deployment, with hybrid work in San Francisco, a competitive base

Qualifications

• Experience in distributed systems, containers, or ML infrastructure.
• Experience with large-scale model training, evaluation, or deployment.
• Publication at a top venue (e.g., ICLR or NeurIPS) is a plus.

Responsibilities

• Build checkpoint and restore of full machine state, including memory and GPU state, across many machines.
• Create the environment orchestrator to start, monitor and shut down thousands of environments.
• Provision real cloud accounts for agents, with hundreds created and deleted on demand.
• Build realistic simulated companies with code, people, tickets, threads and documentation for agent training and evaluation.
• Be the infrastructure foundation that the research and deployed teams depend on.

Skills

Distributed systems

Containers & OS internals

ML infrastructure

Model training

Education

Master's degree in Computer Science

Tools

Checkpointing

Cluster orchestration

Scheduling many heterogeneous jobs

Job description

MaxIT Consulting – Max Corporate Group is seeking a Research Engineer to design and operate the training and environment infrastructure for a fast-growing AI company in San Francisco. You will own long-horizon evaluation environments, post-training infrastructure, and scalable systems used to train and deploy enterprise models.

The role sits on a small founding-stage team, delivering end-to-end infrastructure for research and deployment, with hybrid work in San Francisco, a competitive base