About the Job
We’re big believers in the power of IRL, so for most roles we ask Campers to work from their local Culture Amp office an average of 2 days a week to unlock connection, pace and culture together.
Join us on our mission to make a better world of work.
Culture Amp is backed by leading venture capital funds and has offices in the US, UK, Germany and Australia. Culture Amp has been recognized as one of the world’s top private cloud companies by Forbes and most innovative companies by Fast Company.
Required Skills & Abilities
Experience building and turning production agentic systems, including context engineering, RAG, memory, cost, model selection and performance.
Proven experience analysing the performance of AI or data products in production and turning it into changes that maintained and improved the product.
Hands-on LLM evaluation in production: LLM-as-judge, eval datasets, human-in-the-loop labelling, scoring against thresholds.
Experience with Observability tooling for LLM and agentic systems (traces, sampling, prompt management, production monitoring such as Langfuse or comparable).
Experience with longitudinal measurement: metrics and baselines, regression detection, quality tracking over time.
AI-native daily practice, comfortable using agentic coding tools (Claude Code, Cursor, Codex or similar) on multi-step tasks, with clear judgment on when to direct an agent versus write code yourself.
Strong technical writing and communication, and a track record of building capability into systems and teaching others to own it.
Strong signals: built or scaled an eval and observability practice across multiple teams; evolved existing enterprise codebases with AI; production agentic systems (orchestration, RAG); a postgraduate degree in ML, CS, Applied Maths or related; public writing, talks or open-source work in eval, observability or LLMOps.
You are
Motivated by the effective scaling of AI system performance and adoption in production with the humility to learn in public and the resilience to be a self-starter.
Motivated by enablement. Your biggest wins come from teaching others and building this into our systems, which can mean you do not own what you build forever.
