Staff Software Engineer

STAFF SOFTWARE ENGINEER At UnlikelyAI, we are building the future of AI: one that is reliable, accurate, and transparent. Our neurosymbolic technology harnesses the power of LLMs and generative AI, and combines it with Universal Language – our proprietary symbolic technology that bridges the gap between probabilistic machine learning and deterministic classical computing. Our products are already in use with major enterprises – including tier-1 banks and leading accountancy firms – across audit, compliance, and financial services. In compliance, we combine symbolic decision trees with LLM-powered evidence extraction to catch errors in financial reporting that human reviewers miss. In financial services, we use neurosymbolic guardrails to deliver accurate and explainable outcomes at scale. We are now building toward a platform – a public API and platform experience that will make our core neurosymbolic capabilities available to a broader set of customers and use cases. This is a pivotal moment: we're transitioning from bespoke customer engagements into a scalable product platform, and we need exceptional engineers to help us get there. THE ROLE We are looking for a Staff Software Engineer to help shape the technical direction of our platform as we scale. This is a role for someone who combines deep hands-on engineering ability with the judgement and influence to drive architecture and engineering quality across teams. You'll be one of our most experienced individual contributors – someone the team looks to for guidance on hard technical decisions, system design, and long-term technical strategy. You'll spend most of your time writing code and solving complex problems, but you'll also be expected to identify the highest-leverage work across squads, mentor other engineers, and raise the bar for how we build software. Our core capabilities span symbolic reasoning (decision trees, propositional graphs, knowledge graphs), document ingestion pipelines, and the APIs that expose these to customers. You'll work on genuinely novel problems at the intersection of classical symbolic AI and modern LLMs – for example, how to represent regulatory knowledge as machine-evaluable rules, or how to build feedback loops that improve system accuracy over time. You'll work within a shared monorepo alongside software engineers, research engineers, and applied scientists in a heavily cross-functional environment. We operate in small, focused product teams, supported by shared infrastructure, internal tooling, and an R&D function. WHAT YOU MIGHT WORK ON In your first months, you could find yourself working on any of the following: - Defining the architecture for our new public API – making foundational decisions about authentication, scalability, versioning, and developer experience that will shape the platform for years. - Leading the design and implementation of our document ingestion pipelines to handle new input formats (e.g. PDF, Word) and new regulatory jurisdictions at scale. - Designing evaluation frameworks and benchmarks to measure and improve system accuracy – and establishing these as engineering norms across teams. - Driving improvements to our deployment architecture for enterprise customers with specific cloud and security requirements. - Owning the technical strategy for internal tooling and developer experience across the monorepo – identifying bottlenecks and leading initiatives to address them. - Working on the symbolic reasoning engine that powers our products – including decision tree evaluation, rule generation, and knowledge graph construction. - Identifying and leading cross-cutting technical initiatives that improve reliability, performance, or engineering velocity across the organisation. YOU'LL BE SUCCESSFUL HERE IF... - ...you have deep expertise in Python, including writing well-typed, well-tested code in a collaborative codebase, and strong opinions on how to structure Python projects at scale. - ...you have a proven track