AI Engineer - Data Intelligence - AI Platform
CultureAmpSydneyPosted 19 March 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 building the platform that makes AI possible across Culture Amp. This is an AI Engineer role in our AI Platform team, responsible for the infrastructure, governance, and tooling that enables product teams to ship AI-powered features safely and at scale.
What you'll do
You'll contribute to building and optimising the systems that sit between our product teams and the AI capabilities they need: LLM gateways, vector storage, retrieval infrastructure, and the guardrails that keep it all compliant and cost-effective.
This means:
Developing the platform services that power AI features across Culture Amp, including inference pipelines, embedding storage, and retrieval systems
Implementing a scalable approach to vector search across diverse categories of unstructured data (survey responses, performance feedback, company documents)
Executing LLMOps practices across the team, focusing on enhancing observability, monitoring costs, and ensuring system reliability.
Ensuring AI is used responsibly: implementing guardrails, security controls, and data compliance measures
Partnering with data scientists on the team to productionise models and evaluate new AI capabilities
What makes this interesting
AI is central to Culture Amp's product roadmap. Coach, our AI assistant, is changing how we empower managers and leaders to build better workplaces, and this platform is what makes that possible. You'll be solving genuine scale problems: semantic search across millions of survey responses, retrieval that works across different data types, and governance that keeps pace with how quickly AI capabilities are evolving.
We’re looking for someone who takes ownership of their work and leads by example. You aren't just a strong executor; you are someone who identifies ways to improve our systems, contributes to technical discussions, and collaborates effectively with partner teams. You will play a key role in scaling and evolving how AI infrastructure is built and used at Culture Amp.
You have
Solid platform engineering fundamentals, with experience building and operating services that other teams depend on
Proficiency in Python and experience with ML tooling and infrastructure
Experience with large-scale data systems (streaming, batch processing, data lakes)
Proven experience with ML infrastructure: model serving, vector databases, embedding pipelines
Solid understanding of cloud platforms (AWS preferred) and backend architecture
Experience building and optimising RAG and retrieval systems
The communication skills to work across teams and contribute to technical discussions within and beyond your team
You Are
Self-motivated and able to work mostly independently, comfortable dealing with ambiguity when necessary. You are capable of ensuring that you have everything you need to work effectively and ask for support when required.
Committed to technical excellence, writing high-quality code and contributing to the team's best practices through code reviews and documentation.
Someone who loves collaboration - our teams a ... (truncated, view full listing at source)
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