🛠 Tech Stack

💼 About This Role

You'll own the reliability, observability, and infrastructure foundation for the Knowledge Work team's training environments and evaluations. Your work will ensure stable, well-instrumented systems that proactively surface issues and let researchers focus on research. You'll be the trusted owner of evaluation integrity for model releases.

🎯 What You'll Do

• Serve as dedicated reliability owner for Knowledge Work training environments
• Own canonical evaluation tools and processes for model releases
• Build observability dashboards and operational tooling with high signal-to-noise
• Proactively harden systems via load testing, fault injection, and stress testing

📋 Requirements

• Highly experienced Python engineer shipping production-trusted code
• Experience operating ML or distributed systems at scale with on-call
• Strong SRE or production-engineering mindset
• Foundational ML knowledge to evaluate training environments

✨ Nice to Have

• 5+ years operating ML or distributed systems at scale
• Experience building or operating RL environments, agent harnesses, or LLM evaluation frameworks
• Familiarity with observability stacks and operational dashboard tooling

🎁 Benefits & Perks

• 💰 Competitive salary with $350k-$850k USD range
• 🏢 Hybrid work with San Francisco office
• 🌍 Visa sponsorship offered
• 🧠 Work on cutting-edge AI safety
• 🎓 Bachelor's degree or equivalent required

📨 Hiring Process

Estimated timeline: 2-4 weeks · AI estimate

1. 1Recruiter Screen· 30 min
2. 2Technical Interview· 60 min
3. 3Onsite Interviews· 3 hours