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 OnIn 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 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 record in system design and architecture – you've made foundational technical decisions that shaped the trajectory of a product or platform.
...you've tackled complex algorithms and data structures and have experience working with non-trivial algorithmic problems at scale.
...you care deeply about production-quality engineering – you don't just advocate for software quality, you actively set the standards and build the culture around it.
...you have a track record of technical leadership – you've influenced technical direction across multiple teams or projects without necessarily having direct reports.
...you have significant experience with cloud infrastructure (AWS preferred) – services such as S3, ECR, ECS/EKS, and infrastructure managed via Terraform or similar – and can make informed architectural decisions about deployment and scalability.
...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:
Monorepo experience – comfortable working in and improving a large, shared codebase with multiple product teams contributing.
CI/CD pipelines – hands-on experience with GitHub Actions or similar, ideally including designing and optimising CI infrastructure.
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.
Experience mentoring engineers and helping teams grow their technical capabilities.
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.
Top Skills
What We Do
UnlikelyAI is a deep tech startup working to create a world where highly intelligent automated systems enable humanity to flourish and benefit us all. We are pioneering transformative technology aimed at making Artificial Intelligence more accurate, trustworthy and safe. We are combining the fantastic opportunities created using LLMs with the trust, accuracy and explainability of symbolic/algorithmic methods, which together we call our neurosymbolic platform. Based in London, we were founded by William Tunstall-Pedoe, best known for his role in creating Alexa after Amazon acquired his first startup.
