Senior Applied AI Engineer

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'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. 

• →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.
• →Partner closely with product, design, and people science so features are fit for purpose and scale.
• →Contribute to evaluation frameworks and bias testing that meet enterprise requirements for transparency, fairness, and responsible AI.
• →Create and Monitor guardrails and safety in production.
• →Stay current with AI research, literature and provider offerings, and bring what's useful into production. 

• Proven commercial experience taking ML/AI systems to production.
• Hands-on experience with knowledge graphs and graph databases (e.g. Neo4j, Neptune).
• Production LLM evaluation and observability: LLM-as-judge, eval datasets, human-in-the-loop labelling, scoring against thresholds, plus tracing and production monitoring (e.g. Langfuse, LangSmith).
• Experience in designing and scaling production-grade multi-agentic systems (orchestration, prompt and context engineering, RAG, memory, agent architectures)
• Full stack software development experience, ideally Python, React, PGVector
• Strong software engineering fundamentals - you write clean, tested, maintainable code. 
• Proven ability to instruct and delegate complex workflows to autonomous agents, building the necessary feedback loops to ensure they act reliably on your behalf.
• Hands-on experience designing and integrating data pipelines - you're comfortable moving data across systems, working within rigorous security standards, handling messy real-world inputs, and building robust orchestration
• You are ambitious with what you build whilst maintaining a pragmatic, solution-focused mindset - you evaluate new technology through the lens of "does this scalably solve the problem?" not "is this technically interesting?"
• Values alignment, the initiative to take risks, and the humility to learn and work out loud. You take pride in your work and its successful utilisation, stability, extendability and maintainability. 

• Experience at startups or founding teams - comfortable with ambiguity and fast iteration
• Postgraduate degree (Masters or PhD) in Machine Learning, Computer Science, Applied Mathematics, or related quantitative field
• Evidence of taking cutting-edge (relevant) techniques from paper to production
• Experience with evaluation/benchmarking frameworks for AI systems
• Experience implementing GraphRAG or Knowledge Graphs (e.g., Neo4j, Neptune)
• Evidence of taking relevant, recent techniques from paper to production

• Familiarity with people analytics, HR tech, or behavioural science applications
• Open source contributions or public technical writing

If you require reasonable accommodations or adjustments due to a disability to complete the online application or to participate in the interview process, please contact accommodations@cultureamp.com and identify the type of accommodation or assistance you are requesting. Do not include any medical or health information in this email. The Reasonable Accommodations team will respond to your email promptly.

Culture Amp will retain your CV & personal information for a period of two years (four years for the US) from the date of your application process completion. Culture Amp may contact you in relation to future job opportunities during this time period. For further information please see our privacy policy here or contact privacy@cultureamp.com.