ML Infrastructure Engineer – Distributed Training & Systems

San Francisco CA Hybrid Full-Time Permanent Employment Mid-Level ASAP Start

MaxIT Consulting Max Corporate Group is looking for a Research Engineer to build the training and environment infrastructure of a fast-growing AI company in San Francisco. The company builds evaluation environments and post-training infrastructure for long-horizon enterprise agents and uses that stack to train and deploy custom models for very large technology companies.

It is a small founding-stage team out of top AI labs and technology companies already profitable and with real enterprise revenue. This role sits under the whole company: the research and deployed teams build directly on what you own.

As agent workflows get longer evaluation and training infrastructure become the bottleneck. You will own the systems that let long runs be paused rewound and continued and that start watch and shut down thousands of heterogeneous environments cheaply and reliably.

Within your first 90 days you will own one of the core systems end to end: checkpoint and restore the environment orchestrator or the simulated companies used for training and evaluation.

• Build checkpoint and restore of full machine state including memory and GPU state across many machines.
• Build the 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.
• Be the infrastructure foundation that the research and deployed teams depend on.

• One to five years of full-time experience (at least one year full time).
• Deep experience in at least one of: distributed systems containers and operating-system internals ML infrastructure or model training.
• Has trained or served models at real scale (distributed training inference or serving) or built the environment or benchmark behind a published result.
• A publication at a top venue (such as ICLR or NeurIPS).
• Has built and owned infrastructure that other people depended on beyond a single research script.
• Comfortable choosing the right tools: no specific language or framework is required.
• Professional English as the role works daily with technical teams and enterprise customers.

• Experience with checkpointing cluster orchestration or scheduling of many heterogeneous jobs.
• Compiler inference or serving work on large models.
• Simulation or benchmark environments (robotics sim agent gyms browser or operating-system environments multi-agent benchmarks).
• Masters degree from a strong computer science program.
• Interest in joining an early-stage company for the long-term upside.

Work arrangement: Hybrid three to four days a week in the San Francisco office
Schedule: Full-Time six-day work week
Employment type: Permanent employment
Seniority: Mid-Level (1 to 5 years)
Start date: ASAP
Location: San Francisco California United States

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. Visa sponsorship for new petitions is not available; H-1B transfers and OPT with sufficient time remaining are considered.

If you want to own the infrastructure that frontier-level agent training runs on we would like to hear from you. Apply through this platform including your expected gross annual salary in USD and your availability to start.