ML Infrastructure Engineer – Distributed Training & Systems

Max Corporate Group • San Francisco, CA

2d • Hybrid

About The Position

MaxIT Consulting – Max Corporate Group is hiring an ML Infrastructure Engineer to build training and environment infrastructure for a fast-growing AI company in San Francisco. The company develops evaluation environments and post-training infrastructure for long-horizon enterprise agents, using that stack to train and deploy custom models for large technology companies. The team is described as small, founding-stage, profitable, and generating enterprise revenue.

The role owns systems used by the research and deployed teams, including infrastructure that allows long training runs to be paused, rewound, and continued, and that starts, monitors, and shuts down thousands of heterogeneous environments. Within the first 90 days, the hire will own a core system end to end: checkpoint and restore, the environment orchestrator, or simulated companies used for training and evaluation.

The position is hybrid, with three to four days per week in the San Francisco office, and has a six-day work week. It is permanent, full-time employment at the mid-level, with an ASAP start. Compensation includes a competitive base salary, founding-level equity, a performance bonus tied to customer deployments, and health, dental, and vision coverage. Flexible arrangements for candidates outside the Bay Area are reviewed case by case. Candidates must be legally authorized to work in the United States. New visa petitions are not sponsored; H-1B transfers and OPT with sufficient time remaining are considered.

Requirements

• One to five years of full-time experience, including at least one year of full-time experience.
• Deep experience in at least one of distributed systems, containers and operating-system internals, ML infrastructure, or model training.
• Experience training or serving models at real scale, such as distributed training, inference, or serving, or experience building the environment or benchmark behind a published result.
• A publication at a top venue, such as ICLR or NeurIPS.
• Experience building and owning infrastructure that others depended on, beyond a single research script.
• Professional English, for daily work with technical teams and enterprise customers.
• Legal authorization to work in the United States.

Nice To Haves

• Experience with checkpointing, cluster orchestration, or scheduling many heterogeneous jobs.
• Compiler, inference, or serving work on large models.
• Experience with simulation or benchmark environments, such as robotics simulation, agent gyms, browser or operating-system environments, or multi-agent benchmarks.
• A master's degree from a strong computer science program.
• Interest in joining an early-stage company for the long-term upside.

Responsibilities

• Build checkpoint and restore systems for full machine state, including memory and GPU state, across many machines.
• Build an orchestrator that starts, monitors, and shuts down thousands of environments, from a single container to a multi-GPU cluster.
• 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.
• Provide the infrastructure foundation on which the research and deployed teams depend.

Benefits

• Competitive base salary
• Founding-level equity
• Performance bonus tied to customer deployments
• Health coverage
• Dental coverage
• Vision coverage
• Flexible arrangements for candidates outside the Bay Area, reviewed case by case

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed