Workonomics
Senior Software Engineer
How your CV stacks up
Upload your CV to see how well it fits this job role
?%
Building the AI Infrastructure Layer for Enterprise Data
💫 Company | AI-native developer platform / AI infrastructure
📈 Stage | Series C, $60m ARR, 30k customers, 50% YoY growth
📏 Size | ~300 globally, London office recently opened
🧢 Role | Senior/Staff Software Engineer
🎯 Areas | Agentic systems, LLMs, MCP, evals, prompt engineering
✨ Skills | TypeScript / React, full stack product development
📍 Based | Hoxton, London
💻 Hybrid | 3 days in-office
💰 Offer | £120-150k + stock options (pre-IPO)
Background
Most companies are asking how to use AI. This one already has the answer, and is building it into a product customers pay for.
Their platform holds one of the most valuable things you can feed a LLM: structured, governed enterprise data at scale. 30k companies, including some of the biggest names in tech, run on it. While competitors bolt AI onto legacy systems, this was built for the AI era from day one.
Reasons to use Rodeo
Start with a chat, not a search bar
Grad scheme, placement, apprenticeship? Not sure what you want yet — that's fine. Your agent talks it through with you and turns "I have no idea" into a shortlist.
It searches the market for you
Every day your agent scans the market matching roles against what actually matters to you, not just keywords on a CV.
Only hits
No noise. No "maybe this fits." Just roles with a clear explanation of why they're right — and where to focus when applying.
They're now hiring 2-3 Senior/Staff Software Engineers to join the small team at the centre of it all.
The Team Builds
An AI assistant that deeply understands each customer's data - relationships, rules, business logic. It can interrogate thousands of records in seconds and generate perfectly structured output.
The goal now: turn it from a great feature into a revenue line.
The Role
Full-stack product development, not narrow ML.
React interfaces, APIs, backend services, plus the harder layer underneath: agent orchestration, system prompts (treated here as real engineering, not an afterthought), evals, and the query language that gives the agent its understanding of customer data.
Think, real-time data graphs, agentic workflows, multi-tenant architecture, MCP integrations.
This is for
- An engineer strong in TypeScript and React, with LLM systems shipped in production. You know getting a model to do something once is easy - getting it right for every user is the challenge
- A systems thinker: given a new agent capability, you map user intent, edge cases, failure modes, and what the agent should know, use, and escalate
- A genuinely good reader who cares how English is put together - why one phrasing lands and another doesn't, what "you" refers to, what's implied vs stated outright. An agent's instructions are prose read literally by a system, and writing them well takes a real feel for language, not just correct grammar. A background in linguistics / languages / classics is a strong signal here
- Someone comfortable with probabilistic systems: you run experiments, design evals, read results statistically
Get help with your application
Your very own career expert that helps elevate your application to the next level.
Reach out or apply below and I'll share more about the company and the role.
“It took my CV and asked me questions relevant to understanding what kind of jobs to suggest for me. Suggestions were almost perfect. Jobs were exactly what I’ve been looking for.”
Jessica, London
Skills
Location