You will lead the design and implementation of large-scale, production-grade distributed systems that power AI features for millions of daily users. You'll own the architecture decisions that keep our systems flexible, cost-effective, and robust, direct strategy for distributed systems, and manage technical debt across the AI Products estate.
Beyond architecture, your impact lies in lifting the technical capability of the entire AI Products team. You will champion engineering excellence, mentor junior engineers, and collaborate across Xero to enhance data usability - applying modern AI research, including Large Language Models, only once it can be engineered into reliable, production-grade systems.
The teamYou will join the AI Products group, a diverse team of scientists, engineers, product managers, and analysts within our broader Data & Science division. As a Machine Learning Engineer, you'll partner closely with Applied Scientists to build the interfaces and harnesses that safely and reliably transition models from research into production. Together, this collaborative team reduces toil and delivers beautiful, data-driven insights for small businesses.
The team is currently working on:Designing and building highly scalable, distributed production infrastructure to support generative AI features
Harnessing tools like Python, SQL, and distributed processing engines such as Spark or Dask to handle web-scale data workloads
Deploying to production environments running on AWS and Kubernetes Integrating modern Large Language Model technologies into product features once they're production-ready
Xero offers a flexible hybrid working model designed to blend the collaboration of office life with the autonomy of remote work. You will have access to our modern office spaces, with expectations around office days and collaborative 'boost days' aligned to help your team connect and ship great code effectively.
5+ years building and operating production Python (or equivalent language) services at scale - this is a software engineering role first; strong system design and coding proficiency are non-negotiable
A track record of owning services or pipelines in production, including operational/on-call responsibility, incident response, and managing technical debt over time
Deep understanding of distributed processing principles (Spark, Dask, or similar) alongside strong SQL capabilities
Demonstrated experience integrating ML models or LLM-based features into production systems - you don't need a research background, but you should be comfortable working alongside Applied Scientists to productionize their work
Exceptional communication skills, with the ability to translate complex technical concepts for both business and technical audiences
A natural coaching mindset, with experience establishing engineering standards and mentoring other engineers
Familiarity with ML tooling such as MLFlow, TensorFlow, or PyTorch, and data orchestration tools like Airflow or Prefect, is valued - but production engineering depth matters more than research exposure
Nice to have: prior experience applying or fine-tuning LLMs in a product context, though this is not a substitute for the core software engineering bar above
Apply even if your experience isn't a perfect match! At Xero, we hire based on your skills, passion, and the unique perspective you can bring to enhance our culture and team
Xero London, England Office
In the heart of bustling Kings Cross, we have a lively office only a 5-minute walk from the tube station.
