Role OverviewThe 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. The role sits under the whole company: the research and deployed teams build directly on what you own.
What You Will Do
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.
Why It Might Be a Fit
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.
Requirements
- 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
Benefits
- Competitive base salary
- Founding-level equity
- Performance bonus tied to customer deployments
- Health, dental and vision coverage
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