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, with the research and deployed teams building directly on what the engineer owns.
What You Will Do
Build the training and environment infrastructure, including checkpoint and restore of full machine state, environment orchestrator, and simulated companies for agent training and evaluation.
Why It Might Be a Fit
One to five years of full-time experience, deep experience in distributed systems, containers, operating-system internals, ML infrastructure, or model training, and a publication at a top venue.
Requirements
- One to five years of full-time experience
- 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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