🛠 Tech Stack

💼 About This Role

You'll embed directly with AI companies and research labs as the primary technical interface for our platform. You'll translate customer objectives into RL training runs, agent architectures, and evaluation harnesses using the Lab stack. This role feeds field insights back into our product roadmap.

🎯 What You'll Do

• Design custom RL environments and evaluation harnesses for customer domains.
• Architect agent scaffolding including tool use, multi-step reasoning, and sandbox execution.
• Serve as technical lead for customer engagements end-to-end.
• Codify patterns from engagements into templates and documentation.

📋 Requirements

• Hands-on experience building or deploying LLM-based agents in the past 1-2 years.
• Strong intuition for evaluation design and reward signal construction.
• Working understanding of RL and post-training concepts (GRPO, RLHF, SFT).
• Python skills and comfort with Hugging Face, inference engines, and agent frameworks.

✨ Nice to Have

• Experience with agent frameworks like DSPy, LangGraph, or MCP.
• Experience running LLM evaluation pipelines at scale.
• Familiarity with sandbox/code execution environments for agent evaluation.

🎁 Benefits & Perks

• 💰 Cash compensation $150-300k + equity incentives.
• 🏢 Flexible work in San Francisco or hybrid-remote.
• 🌎 Visa sponsorship & relocation support.
• 📚 Professional development budget.
• 🎉 Team off-sites & conference attendance.