Backend Software Engineer — Applied ML & LLM Systems

About this role

About Dwelly Dwelly is building the AI operating system for residential lettings. Its growing network of agencies provides its AI with real-world data, continuous feedback, and control over complete workflows, making exceptional service the standard for landlords and tenants. Today, Dwelly operates more than 15,000 properties and $470 million in GMV, making it one of the UK’s ten largest lettings operators. The company has raised $263 million.

We’re a fast-growing, product-focused company, backed by top-tier investors and led by a team with deep experience in real estate, technology, and operations. Position Summary We are looking for a Backend Engineer with strong applied ML experience to build production systems that extract, enrich, summarise and structure information from emails, documents and other unstructured data. This is not a pure data science or research role.

It is a production engineering role focused on building reliable Python backend services around NLP, retrieval and LLM-powered workflows. You will work on practical problems such as extracting useful information from email correspondence during agency migrations and summarising a client’s full communication history inside their Dwelly profile. The right person is comfortable working with messy real-world data, taking prototypes into production, measuring quality and improving systems through evaluation and feedback loops.

We’re hiring remotely across the UK, Ireland, and European time zones . Candidates should be based within these regions/time zones to enable effective collaboration with the wider team. What You’ll Do Build systems that extract structured data from emails, documents and other unstructured sources. Enrich migrated client, landlord, tenant and property records with useful information from communication history. Develop solutions that summarise a client’s full email history and surface the most relevant context inside Dwelly.

Build production NLP / ML-backed backend services that work reliably on messy real-world data. Improve retrieval and ranking systems using approaches such as RAG, BM25, embeddings, hybrid search and reranking. Define quality metrics, evaluation datasets and feedback loops for extraction, summarisation and retrieval systems. Build Python backend services and APIs using frameworks such as FastAPI, Django, Flask or similar.