Job Description The Machine Learning (ML) Engineer will undertake technical work to design, prototype and develop LLM-based products that utilise Earth Observation data, primarily Nearmap imagery and AI data. Where a data scientist will typically focus more on the meaning in the data, and producing accurate models, the ML Engineer focusses on integrating models and data into complex workflows that autonomously solve problems. The role requires solving a variety of challenging problems in software engineering, agentic AI design and evaluation, and close collaboration with data scientists as a peer. Key Responsibilities Performs software engineering tasks required by an end-to-end AI system. Designing, building and maintaining subcomponents of an AI system, in collaboration with data scientists: microservices, APIs, working with technologies such as Docker, Kubernetes, MCP. Collaborating with other engineering teams and DevOps to ensure consistent best practices and integration of systems. Reviews code and work of peers. Personal Attributes we love to see: Pragmatism : While extensive knowledge of ML theory is highly valued, pragmatism wins over elaborate theory when it comes to shipping products that work. Collaboration : We believe data science is a team sport, and are after candidates who can communicate well, share knowledge, and be open to taking on ideas from anyone in the team. Having worked on shared code-bases in a commercial environment is a big plus, but it's the attitude that matters most. Technical Skills : A decent base of python and linux are key to a role in the team. Other than that, we're pretty flexible - we know tools are changing rapidly, and will continue to do so for many years to come. Experience with tools like Kubernetes, Helm, PyTorch, Terraform, Prometheus etc. are highly valued, but not mandatory. Attention to detail : Showing attention to detail when it counts is important.
