Staff Applied AI Scientist

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 .

We are looking for a Staff Applied AI Scientist to join the team behind AI Coach, Culture Amp’s contextually-aware AI coaching system that turns survey insights, performance data and interpersonal dynamics into personalised assistance at scale. Shipping an AI product is only the beginning. The harder problem, and one few teams have solved, is measuring on an ongoing basis whether that product is working well in production, why its quality changes, and how to make it better. In this role you will scale the effective measurement and improvement of our AI products in production, which means establishing online evaluation of live AI features, and then to make this sustainable by enabling the rest of our engineering org to do the same.

As part of this team of amazing humans,

You will

• Design and run sampling, LLM-as-a-judge, and labelling systems over de-identified production traces (for example, with Langfuse) to build longitudinal evaluation monitoring and alerting.
• Build LLM-powered analysis that works out why performance moved and recommends prompt or system changes to improve the product.
• Own the full feedback loop: prompt engineering, evaluation at scale, data labelling and continuous improvement.
• Enable others through reusable frameworks, tooling and documentation so product and engineering teams run their own evaluations. Lead from the front, then hand over.
• Partner closely with Coach, product, data science and people science so measured quality maps to real customer value.
• Stay current with the latest evaluation, observability and LLMOps research and provider offerings.

You have

• 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: L