Research Engineer
What you need
- Experience with containers, data pipelines, experiment infrastructure
- Hands-on RL, model post-training, or agent evaluation; PyTorch
- Experimental judgment: clear baselines, controlled comparisons, failure analysis
- Track record using coding agents to build substantial software
What you'll do
- Build multi-step tasks, runtimes, verifiers, Harbor-format environments
- Develop data generation, orchestration, rollout, evaluation pipelines at scale
- Run controlled experiments on task difficulty, reward design, agent behavior
- Analyze failures, reward exploitation; build generalization evaluations
- Work with domain experts to build reproducible training environments
About Klavis AI
Klavis AI builds RL environments and high-quality training data for frontier AI models. We work across long-horizon coding, AI4Science, AI4AI, knowledge work, and agentic tool use.
The role
We are hiring a Research Engineer to turn questions about agent learning into reliable systems and reproducible experiments. Work directly with the founders to build environments, datasets, and evaluations, and study how task design, rewards, and verification affect model capabilities.
What you will work on
• Build realistic multi-step tasks, isolated runtimes, reference solutions, and verifiers, including environments in Harbor format.
• Develop pipelines for high-quality data generation, environment orchestration, agent rollouts, evaluation, and quality control at scale.
• Run controlled experiments on task difficulty, curricula, reward design, and long-horizon agent behavior.
• Analyze failures and reward exploitation, and build evaluations that measure generalization.
• Work with domain experts to turn scientific and knowledge-work workflows into reproducible training environments.
What you bring
• Strong experience with containers, data pipelines, and reliable experiment infrastructure.
• Hands-on work with reinforcement learning, model post-training, or agent evaluation; experience with PyTorch or a similar framework.
• Careful experimental judgment: clear baselines, controlled comparisons, and useful failure analysis.
• A track record of using coding agents to build, test, and debug substantial software.
Show us your work
Share a system, experiment, benchmark, paper, or open-source contribution you are proud of. Explain what you built, what failed, and what you learned.
Location and compensation
Full-time. San Francisco or remote within the United States. Annual base salary: $150,000–$200,000 USD. This role does not include equity.
Apply through YC with your CV and links to your work.
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About Klavis AI
Powering frontier AI labs with real world MCP environments and complex, long-horizon agentic tool-use data.
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