Senior Applied AI Engineer in Sydney

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.

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.

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.
• 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.

You have:

• 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.
• Values alignment, the initiative to take risks, and the humility to learn and work out loud.

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) techniques from paper to production.
• Experience with evaluation/benchmarking frameworks for AI systems.
• Experience implementing GraphRAG or Knowledge Graphs (e.g., Neo4j, Neptune).

Bonus:

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

You'll work at scale on hard problems that matter - building AI that helps organisations develop their people, improve feedback culture, and foster the conditions for sustainable high performance.