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The company
Every physical product, every car, drug, turbine and aircraft, has a failure model. A structured document that says: here's how this thing can break, how bad it is when it does, and what we do to catch it. Millions of them exist. They're all wrong.
They're wrong because they're written once in a room, then never connected to what actually happens in the field. When reality proves the model wrong, with a failure it didn't predict or a control that doesn't work, nobody finds out until something breaks.
We built the system that connects the failure model to the evidence that tests it. Continuously. Across design, manufacturing, and field operations.
We call it continuous risk intelligence and nobody else has built it.
We're deploying with global engineering teams and scaling across pharma, automotive, chemicals and more.
The role
- Week 1: ship a production feature for a live customer deployment. Real users, real data, real impact.
- Month 1-3: own core platform modules: knowledge graph construction, industrial NLP pipeline, agentic FMEA generation, statistical models (Weibull, Bayesian updating), human feedback loops.
- Month 3-12: scale the system across new industries and customers. Architecture decisions are yours.
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.
Your code ships to Fortune 50 customers. You talk to the engineers who use it. You own the architecture.
The stack
- Python
- AWS/Azure/GCP/Custom
- Knowledge graphs
- LLMs for industrial unstructured data (manuals, reports, logs, P&IDs, technical drawings/3D models, time-series data and simulations)
- Weibull and Bayesian statistical models
- Agentic workflows with chain-of-thought reasoning
- Human-in-the-loop feedback systems
The problem is genuinely hard: messy unstructured industrial text, domain-specific inference, probabilistic reasoning over failure modes, graph construction from heterogeneous data sources.
Who you are
- 2-5 years experience. Shipped something complex super fast, and hungry for world-class impact.
- Set your own priorities and deliver. Don't need someone telling you what to do next.
- See equity in a company at this stage as the reason to join, not a bonus on top of salary.
Get help with your application
Your very own career expert that helps elevate your application to the next level.
This is a full-time, equity-bearing, building-the-company role. Not a project, not a side gig.
Terms
- Cash plus equity. Numbers on the first call.
- Equity: up to 5%, 4-year vest, 1-year cliff, granted ahead of our next round
- Location: Remote, Europe
Process
- Call 1 (30-45 min): What did you build, how fast do you ship, what would make you go all-in.
- Call 2 (30 min): Comp, start date, paid trial logistics.
- Paid trial week (£500-1000): real customer problem, real codebase. Face-to-face.
The stakes
Boeing 737 MAX: 346 dead. Takata airbags: 27 dead. Every recall traces back to an incomplete risk model. 2.7M manufacturing workers retiring this decade, taking tribal knowledge with them. The US government named "tacit engineering knowledge" as the number one bottleneck in reshoring semiconductor manufacturing.
We make risk models stay true. The technical problem is hard. The stakes are physical. The data moat compounds with every deployment.
“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
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