Applied AI Engineer

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 the world’s leading employee experience platform, revolutionizing how 25 million employees across more than 6,000 companies create a better world of work. Culture Amp empowers companies of all sizes and industries to transform employee engagement, drive performance management, and develop high-performing teams. Powered by people science and the most comprehensive employee dataset in the world, the most innovative companies including Canva, On, Asana, Dolby, McDonalds and Nasdaq depend on Culture Amp every day.

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.

For more information visit cultureamp.com.

How you can help make a better world of work

We're looking for a Senior Applied AI Engineer to join the Frontier team, helping build Agentic AI Solutions at Culture Amp. You'll work at the intersection of applied AI research and product engineering, translating cutting-edge techniques into production systems that genuinely improve how people experience work. This isn't a research lab - you'll be shipping features that solve real customer problems. 

What you'll do

• Design, implement and evaluate areas and features for agentic AI capabilities and systems using frameworks (e.g., LangGraph) for stateful, multi-turn conversations.
• Advise, help build, and orchestrate data pipelines and integrations involving workplace data (surveys, goals, performance reviews, feedback, 1-1 notes) into coherent context view.
• Build memory across sessions, and context graph, grounded in a custom ontology, alongside per-user relational and procedural memory and preferences that persist to the user profile.
• Build and evaluate effective RAG, including pre-processing and hybrid search, and design areas of our graph-based agentic frameworks.
• Own the end-to-end feedback loop: prompt engineering, evaluation at scale, and continuous improvement, including LLM-powered analysis tools that diagnose performance shifts and recommend prompt or system-level changes.
• Translate customer requirements into technical solutions by working backwards from user needs to system design, and write the technical documentation that supports transfer to product teams.