About Us 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

To meet the demands of our growing commercial momentum, we are looking for a smart, dedicated senior software engineer to join our team. We want someone who thrives on diving deep into code to solve challenging and novel problems. You will have extensive software engineering experience, with exceptional coding ability, ideally including experience in high-growth start-ups.

This role will play a major part in developing our core capabilities, which 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:

• Building the infrastructure for our new public API, including authentication, scalability, and developer documentation.
• Improving our document ingestion pipelines to handle new input formats (e.g. PDF, Word) and new regulatory jurisdictions.
• Developing evaluation frameworks and benchmarks to measure and improve system accuracy.
• Scaling our deployment approach for enterprise customers with specific cloud and security requirements.
• Improving internal tooling and developer experience across the monorepo.
• Working on the symbolic reasoning engine that powers our products – including decision tree evaluation, rule generation, and knowledge graph construction.

You'll be successful here if...

• ...you have strong proficiency in Python, including writing well-typed, well-tested code in a collaborative codebase.
• ...you've tackled complex algorithms and data structures and have experience working with non-trivial algorithmic problems.
• ...you care deeply about production-quality engineering – you have a track record of advocating for software quality, improving engineering standards, and championing best practices.
• ...you thrive with end-to-end ownership – you've led the process from ideation to production for brand-new software systems.
• ...you have experience with cloud infrastructure (AWS preferred) – services such as S3, ECR, ECS/EKS, and infrastructure managed via Terraform or similar.

Other Skills You don't need to tick every box below, but any of the following would strengthen your application:

• Monorepo experience – comfortable working in a large, shared codebase with multiple product teams contributing.
• CI/CD pipelines – hands-on experience with GitHub Actions or similar.
• Experience with document processing pipelines – PDF parsing, OCR, structured data extraction.
• Familiarity with knowledge representation – decision trees, knowledge graphs, ontologies, or symbolic reasoning systems.
• Experience with LLM integration in production systems – prompt engineering, evaluation, working with APIs such as Gemini, Claude, or OpenAI.

How We Work

We're a team of around 30 people based primarily in the UK. We operate a hybrid working policy, with three days a week in our Central London office. Engineering is organised into product-focused squads, supported by shared infrastructure and an R&D function. We work in a monorepo, deploy to AWS, and care deeply about developer experience – we're actively investing in modernising our tooling, CI, and repository structure.

We run hackathons, we have strong opinions about code quality (held loosely), and we ship often. Our culture is collaborative and low-ego: engineers regularly move between teams, pair on hard problems, and contribute ideas regardless of seniority. We take the work seriously, but not ourselves.