Klavis AI Verified
Artificial Intelligence, Legal Tech, FinTech, Document Automation
Research Engineer
RemoteRemoteFull Time$150,000–$200,000 /yrPosted 2 days agoHidden Gem · YC Startup
Required skills
- RL environments
- training data
- agent learning
- model post-training
- agent evaluation
- PyTorch
- containers
- data pipelines
- experiment infrastructure
- coding agents
Nice to have
- AI4Science
- AI4AI
- agentic tool use
- task design
- rewards
- verification
Role summary
Klavis AI is seeking a Research Engineer to build reliable systems and reproducible experiments for agent learning environments and training data generation. This role involves creating multi-step tasks, developing data pipelines, running controlled experiments on various aspects of agent behavior, and analyzing failures. The ideal candidate will have strong experience with containers, data pipelines, experiment infrastructure, and reinforcement learning, with proficiency in PyTorch or similar frameworks. A track record of using coding agents to build and debug software is also required. The position is full-time, based in San Francisco or remote within the US.
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.
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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