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 talented engineers to help us get there.
We are looking for a motivated, capable software engineer to join our growing team. You'll bring solid engineering fundamentals and a genuine enthusiasm for writing clean, well-tested code. You might have a few years of professional experience under your belt – enough to have built and shipped real software, but with plenty of room and appetite to grow.
This role will contribute to 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 – and you'll be supported by experienced engineers and researchers as you do.
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.
In your first months, you could find yourself working on any of the following:
Building and extending our 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.
Contributing to evaluation frameworks and benchmarks to measure and improve system accuracy.
Supporting 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 have proficiency in Python, including writing well-typed, well-tested code in a collaborative environment.
...you have a solid grasp of algorithms and data structures and are comfortable reasoning about complexity and solving non-trivial problems.
...you care about software quality – you write tests, review code thoughtfully, and take pride in shipping well-crafted software.
...you have delivery experience – you've contributed meaningfully to shipping software to production, not just prototypes.
...you have a bias for action – you move quickly, make informed decisions, and iterate without waiting for perfect information.
...you have a relevant degree in Computer Science, Mathematics, Engineering, or STEM – or equivalent practical experience.
You don't need to tick every box below, but any of the following would strengthen your application:
Cloud infrastructure (AWS preferred) – familiarity with services such as S3, ECR, ECS/EKS, or infrastructure-as-code tools like Terraform.
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.
Frontend experience with React and TypeScript – we value engineers who can contribute across the stack when needed.
Experience in regulated industries – fintech, audit, compliance, insurance, or banking.
Familiarity with the modern Python tooling ecosystem: uv for package management, ruff for linting, pyright or similar type checkers.
Experience with observability and monitoring tools such as Datadog.
Start-up experience – you thrive in fast-moving environments where priorities shift and ownership is broad.
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.
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