Model ML
Member of Technical Staff - AI

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Company Overview
Model ML is the AI workflow builder transforming how major financial institutions produce and validate client-ready work. Model ML converts complex, manual processes into fully automated AI systems that scale across global teams. In under a year, Model ML has become one of the fastest growing enterprise AI platforms worldwide and recently closed a $75 million Series A, one of the largest fintech Series A rounds ever. The round was backed by FT Partners, Y Combinator, LocalGlobe, QED, 13books, and other top global investors.
Job Description
In this role, you will own and drive large portions of our AI agent infrastructure, from designing and deploying multi-agent systems to integrating Retrieval-Augmented Generation (RAG) pipelines, and evaluation frameworks. You will be responsible for delivering AI-powered features into production at scale — ensuring they are performant, reliable, and secure — while also contributing across the stack, from frontend interfaces to backend APIs, databases, and deployment pipelines.
Responsibilities
- Build, test, and deploy backend services and APIs (Python/ Django/ FastAPI preferred, but other languages/frameworks welcome).
- Collaborate with founders, growth team, designers, and other engineers to deliver high-impact features.
- Ensure scalability, performance, and security across the stack.
- Develop and deploy AI-powered features in production, including RAG (Retrieval-Augmented Generation) systems, multi-agent infrastructure, and evaluation frameworks (Evals).
- Create data pipelines for AI model training, evaluation, and continuous improvement.
- Mentor junior developers and promote engineering best practices.
Reasons to use Rodeo
I’m in my final year doing Economics and I don’t know whether to apply for grad schemes now or do a masters first. What do you think?
Honest answer — it depends on where you want to end up. A lot of top grad schemes (Big 4, civil service, banking) don’t need a masters. Let’s look at the ones you’d be competitive for now, and we can decide if a masters actually adds anything.
Also worth knowing: most autumn 2026 applications are open now. Timing matters more than you think.
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.
Graduate Consultant — 2026 Scheme
Why you're a good match
StrongYour economics background and your summer at a regional bank line up with what PwC looks for on the consulting scheme. Applications close in four weeks.
See breakdownIt 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.
Why you're a good match
You’ve got the grades and the economics background, and your bank internship is exactly the experience this scheme looks for. Apply soon — deadlines close within the month.
Experience fit
Your summer at the bank plus your econometrics coursework map directly to the day-one responsibilities on this scheme — client modelling, market briefings, and deal support.
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.
What You Can Expect
It won't be easy; in fact, it will be very hard.
BUT, it will be a lot of fun.
You need to be comfortable in being uncomfortable; timelines will change, priorities will most likely shift
Be prepared to sacrifice your work-life balance in exchange for joining an incredible journey and learning a lot along the way.
Requirements
- 5+ years of professional software engineering experience.
- Hands-on experience building and deploying AI applications in production environments.
- Strong backend development skills (Python preferred).
- Solid understanding of relational databases.
- Experience with Git and collaborative development workflows.
- Knowledge of cloud infrastructure, containerization (Docker, Kubernetes), and CI/CD pipelines.
- Strong problem-solving skills and a passion for building great products.
- Experience implementing background workers and task queues (Celery, RQ, etc.).
- Proficiency with Redis for caching, pub/sub, or job queues.
- Hands-on experience building and deploying AI applications in production environments.
- Experience implementing RAG pipelines, AI agent orchestration, and performance monitoring.
- Familiarity with LLM evaluation techniques and tools for measuring model accuracy, reliability, and safety.


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What We Offer
- You will be reporting directly to the founders, who have two successful venture-backed exits under their belt.
- Competitive salary + equity
- Supportive and innovative work environment
About The Interview
Our Process: We're very conscious of everyone's time, so we want to make the process as efficient as possible.
- Call 1: 30-minute intro call with our Talent Acquisition team
- Call 2: 30-minute technical screen
- Call 3: 20-minute systems design deep-dive
- Call 4: Onsite interview with Engineering Leadership
“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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