Senior Engineer - Applied AI

Culture Amp
MelbournePosted 23 February 2026

Job Description

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 6,500 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 Engineer to join the AI Research Experience team, helping build AI Coach - Culture Amp's contextually-aware AI system that integrates survey insights, performance management, and interpersonal dynamics to deliver personalized support to organizations, teams, and individuals at scale. 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 Staying up to date with current research, literature and provider offerings. Design, implement and evaluate areas and features of our multi-agentic platform and systems using modern frameworks (e.g., LangGraph) for stateful, multi-turn coaching conversations. Advise, help build, and orchestrate data pipelines that unify fragmented workplace data (surveys, goals, performance reviews, feedback, 1-1 notes) into coherent coaching context Translate customer requirements into technical solutions - working backwards from user needs to system design. Creating technical documentation to support literacy, technical transfer and engineering practice. Partner closely with product, design, and people science to ensure the features developed are fit for purpose, and in-line with the existing product expectations to ensure seamless partnership transfer of technology. Contribute to evaluation frameworks and bias testing to meet enterprise requirements for transparency and fairness Build cutting-edge, working prototypes, integrated into our environments which validate new approaches and accelerate learning. What we're looking for Must Haves: Proven commercial experience taking ML/AI systems to production. Strong technical writer and communicator Full stack software development experience, ideally Python, React, PGVector Strong software engineering fundamentals - you write clean, tested, maintainable code. You leverage Software development AI tooling effectively. Experience with LLM application development (prompt and context engineering, RAG, agent architectures) 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 A pragmatic, solution-focused mindset - you evaluate new technology through the lens of "does this scalably solve the problem?" not "is this technically interesting?" You fit our values with the proactivity to take risks, and the humility to learn in public. You take pride in your work and its successful utilisation, stability, extendability and maintainability. Strong signals: 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) tec ... (truncated, view full listing at source)
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