Senior Software Engineer, Unity

• Strong software engineering fundamentals and experience building and shipping production systems
• Hands-on data engineering experience: SQL, data modelling, warehouse or lakehouse design, and building pipelines that other people depend on
• Experience working with a cloud data warehouse, ideally BigQuery, and with a pipeline orchestration framework
• Experience with LLM and agent systems, or a clear pull towards them, especially retrieval quality and how you measure whether an answer is any good
• Comfort working without a fully specified brief, and a bias towards putting something usable in front of users early
• Genuine enthusiasm for working alongside data scientists and analytics users, and for shaping the product around how they actually work

Desirable

• We are hiring a Senior Software Engineer to work on an internal AI product analytics agent here at Unity. This lets teams across Unity ask questions of our product data in natural language, used by product managers, engineers, analysts and leadership
• This role will join the data-facing half of the team. You will spend half of your time working on the analytics datasets and pipelines the agent depends on, and the other half on the agent capabilities that sit on top of them: retrieval, knowledge, evaluation and answer quality. This agent is only as good as the data and the knowledge layer beneath it, and you will own that layer
• You will work day to day with data scientists and analysts who are both your closest collaborators and your users. Requirements are still taking shape, so you will have a lot of say in what gets built
• Note: This role is one of two we're hiring on the team. If you lean more towards distributed systems, infrastructure and production operations than towards data modelling and pipelines, take a look at our Senior Backend Engineer, AI Platform & Infrastructure role instead!
• Own and improve the analytics datasets that the agent queries, including data modelling, semantic definitions, and the documentation and metadata that make those datasets legible to an LLM
• Build and maintain the ETL and transformation pipelines that feed those datasets, and raise the bar on their correctness, freshness and testability
• Improve how the agent finds and uses knowledge: knowledge base search, retrieval quality, context construction and prompt history
• Build and extend the evaluation systems that tell us whether the agent is answering correctly, and use them to drive measurable quality improvements
• Develop backend agent workflows covering prompt handling, orchestration and response generation
• Build user-facing features that make analytics workflows faster for technical and non-technical colleagues alike
• Partner with data scientists to turn recurring analytics needs into reusable, scalable capabilities rather than one-off answers
• Help set the roadmap and technical direction for the data and quality side of the platform