Founding AI Engineer

Bishopsgate
3 weeks ago
Create job alert

Founding AI Engineer
Up to £130k + equity
London (5 days on-site)

Be part of the founding team at an early stage Fintech.
Best suited to someone who enjoys building and shipping.
Opportunity to build an AI native toolkit from scratch. 
I’m looking for a Founding AI Engineer to join a very early stage (Pre Seed) startup in London. This role is best suited to people who thrive working in highly ambiguous environments and are happy to pivot at the drop of a hat.
 
Startup life isn’t for everyone, so you do really need to be someone that gets excited by the idea of wearing many hats and getting stuck in.
 
The good news is that the business has two years of runway based on funding alone, the even better news is they’re already revenue generating!
 
Being part of the founding team means you’ll have the opportunity to build an AI native toolkit from the ground up. If having a tangible impact on the core product and overall success of the business is something excites you, then this role is for you.
 
The preferred option is to find people who have come through the software engineering route into AI, as opposed to the more traditional route of Data Scientist/ML Engineer. By this I mean you’ll need to be comfortable writing and shipping code and working on AI APIs, less so model building, fine tuning LLMs etc.
 
Essential requirements:

Founder type mindset with a strong product lens.
You value speed and scale over perfection.
Highly autonomous.
Experience building AI agents/agentic systems/architecture/RAG pipelines.
Software engineering background.
Experience developing and deploying production application layer products.
Enjoy the buzz of startup life and want to work with high energy people. 
Just to highlight, this role is 100% on-site. You will need to be happy being in the office more often than not.
 
Unfortunately, sponsorship is not available for this role.
 
Reach out to Jamie Forgan for more information

Related Jobs

View all jobs

Co-Founder / CTO Opportunity – AI Tech Recruitment Start-Up

Co-Founder / CTO Opportunity – AI Tech Recruitment Start-Up

Machine Learning Engineer, London

Lead Machine Learning Engineer, Associate Director, London

Machine Learning Engineer, London

Mid/Senior Backend Engineer (Node.js & TS)

Get the latest insights and jobs direct. Sign up for our newsletter.

By subscribing you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

Portfolio Projects That Get You Hired for Machine Learning Jobs (With Real GitHub Examples)

In today’s data-driven landscape, the field of machine learning (ML) is one of the most sought-after career paths. From startups to multinational enterprises, organisations are on the lookout for professionals who can develop and deploy ML models that drive impactful decisions. Whether you’re an aspiring data scientist, a seasoned researcher, or a machine learning engineer, one element can truly make your CV shine: a compelling portfolio. While your CV and cover letter detail your educational background and professional experiences, a portfolio reveals your practical know-how. The code you share, the projects you build, and your problem-solving process all help prospective employers ascertain if you’re the right fit for their team. But what kinds of portfolio projects stand out, and how can you showcase them effectively? This article provides the answers. We’ll look at: Why a machine learning portfolio is critical for impressing recruiters. How to select appropriate ML projects for your target roles. Inspirational GitHub examples that exemplify strong project structure and presentation. Tangible project ideas you can start immediately, from predictive modelling to computer vision. Best practices for showcasing your work on GitHub, personal websites, and beyond. Finally, we’ll share how you can leverage these projects to unlock opportunities—plus a handy link to upload your CV on Machine Learning Jobs when you’re ready to apply. Get ready to build a portfolio that underscores your skill set and positions you for the ML role you’ve been dreaming of!

Machine Learning Job Interview Warm‑Up: 30 Real Coding & System‑Design Questions

Machine learning is fuelling innovation across every industry, from healthcare to retail to financial services. As organisations look to harness large datasets and predictive algorithms to gain competitive advantages, the demand for skilled ML professionals continues to soar. Whether you’re aiming for a machine learning engineer role or a research scientist position, strong interview performance can open doors to dynamic projects and fulfilling careers. However, machine learning interviews differ from standard software engineering ones. Beyond coding proficiency, you’ll be tested on algorithms, mathematics, data manipulation, and applied problem-solving skills. Employers also expect you to discuss how to deploy models in production and maintain them effectively—touching on MLOps or advanced system design for scaling model inferences. In this guide, we’ve compiled 30 real coding & system‑design questions you might face in a machine learning job interview. From linear regression to distributed training strategies, these questions aim to test your depth of knowledge and practical know‑how. And if you’re ready to find your next ML opportunity in the UK, head to www.machinelearningjobs.co.uk—a prime location for the latest machine learning vacancies. Let’s dive in and gear up for success in your forthcoming interviews.

Negotiating Your Machine Learning Job Offer: Equity, Bonuses & Perks Explained

How to Secure a Compensation Package That Matches Your Technical Mastery and Strategic Influence in the UK’s ML Landscape Machine learning (ML) has rapidly shifted from an emerging discipline to a mission-critical function in modern enterprises. From optimising e-commerce recommendations to powering autonomous vehicles and driving innovation in healthcare, ML experts hold the keys to transformative outcomes. As a mid‑senior professional in this field, you’re not only crafting sophisticated algorithms; you’re often guiding strategic decisions about data pipelines, model deployment, and product direction. With such a powerful impact on business results, companies across the UK are going beyond standard salary structures to attract top ML talent. Negotiating a compensation package that truly reflects your value means looking beyond the numbers on your monthly payslip. In addition to a competitive base salary, you could be securing equity, performance-based bonuses, and perks that support your ongoing research, development, and growth. However, many mid‑senior ML professionals leave these additional benefits on the table—either because they’re unsure how to negotiate them or they simply underestimate their long-term worth. This guide explores every critical aspect of negotiating a machine learning job offer. Whether you’re joining an AI-focused start-up or a major tech player expanding its ML capabilities, understanding equity structures, bonus schemes, and strategic perks will help you lock in a package that matches your technical expertise and strategic influence. Let’s dive in.