Founding Engineer
Klavis AI · US ·
- Seniority
- Lead
- Category
- Software engineering
- Experience
- 3+ years
- Visa sponsorship
- Yes
- Company size
- 1-10
Klavis AI · US ·
Klavis AI is building high-quality agentic and coding data for frontier AI post-training.
Frontier models are increasingly bottlenecked not just by compute, but by the quality of the coding environments, trajectories, rubrics, rewards, and verification data used to train them. We build that data layer: long-horizon coding tasks, terminal-based software engineering environments, hidden-test verification, expert rubrics, gold trajectories, dockerized environments, and agentic tool-use workflows ready for RL and SFT. We already work with multiple frontier AI labs on production coding and agentic data.
Klavis is founded by Xiangkai Zeng and Zihao Lin. Xiangkai was a Senior Software Engineer on Google Gemini at Google DeepMind, where he built function-calling infrastructure, shipped agentic features, and co-authored the Gemini paper. Zihao was a Senior Software Engineer and Tech Lead at Lyft Recommendations ML and Nordstrom Data Infra, where he built products and infrastructure serving millions of users.
We’re hiring a founding engineer who is genuinely exceptional at using LLMs and coding agents to build, test, debug, and ship real software.
You should be the kind of engineer who can make Claude Code, Codex, MCP tools, shell environments, custom evals, Docker, and agent pipelines feel like an extension of your hands. We are not looking for someone who has only tried basic ChatGPT prompts or simple API wrappers. We are looking for someone who already uses AI agents to build, debug, refactor, test, and ship faster than traditional engineering teams.
You’ll work directly with the founders to build the systems and datasets that help frontier labs train better coding and tool-use agents. In your first 30 days, you’ll onboard into our internal systems, ship improvements to them, and personally produce high-quality tasks end-to-end. By the end of the first month, you should understand what makes a task valuable for frontier post-training. In 90 days, you’ll own a core product or infrastructure area from design to production. You’ll help define what “excellent” agentic and coding data means, build systems that scale production, and directly influence how frontier labs train their next-generation agents.