At a Glance
- Tasks: Build AI agents that streamline revenue team tasks and tackle messy GTM data.
- Company: Join a pioneering tech company focused on innovative AI solutions.
- Benefits: Enjoy 25 days holiday, competitive salary, and remote work flexibility.
- Other info: Opportunity to lead a team and influence company culture.
- Why this job: Be the first engineer shaping AI technology and making a real impact.
- Qualifications: 5+ years in backend engineering with a focus on data-heavy systems.
The predicted salary is between 70000 - 87500 £ per year.
We're building AI agents for GTM — agents that do the work of a revenue team end-to-end: researching accounts, drafting outreach, updating the CRM, triaging inbound, prepping calls, following up. GTM data is the messiest data in the enterprise. It lives across 10+ systems, it's stale, it's permissioned, and full of half-truths people typed into Salesforce on a Friday afternoon. Building agents on top of this without hallucinating and actually getting the job done at scale is the real engineering problem.
You'd be the first engineer. The hard part isn't the agent loop — it's the data and context layer underneath: pulling from Salesforce, HubSpot, Gong, Slack, Notion, Drive and a long tail of broken APIs, then turning that mess into the precise, fresh, permission-aware context an agent needs to take a real action. Most of your hardest engineering time will be on the data side: ingestion, modelling, freshness, retrieval quality, evals, latency, permissions.
Own the data and context pipeline end-to-end: connectors, ingestion, schema modelling, chunking, embedding, indexing, hybrid search, reranking, serving. Own the agent runtime on top: tool use, planning, multi-step execution, error recovery. Build the eval harness before shipping anything new. We don't ship on vibes. By month six you'll have hired the second engineer and shaped the team's culture around data quality, evals, latency, and product taste.
Requirements:
- 5+ years of production backend experience, with meaningful time on data-heavy systems — retrieval, search, recsys, streaming pipelines, or ML infra with real users and real SLAs.
- You think data first. Bad data in, bad agents out, and you've felt that pain.
- Shipped a non-trivial RAG, semantic search, or agent system end to end. Strong opinions on hybrid retrieval. You know when BM25 beats embeddings, when SQL beats both, and you've made those calls in production.
- (Nice to have) Python or Go fluency.
- Multi-tenant systems with row- or document-level access control.
- Messy GTM stack integrations — Salesforce, HubSpot, Gong, Outreach, Apollo — and the scars to show for it.
- Data governance or security overlap: lineage, audit trails, PII handling, prompt injection defence.
Founders call (60 min, remote). Walk us through a data-heavy system you've built. 25 days holiday + UK.
AI / Machine Learning Specialist in London employer: Stealth AI Company
Join a pioneering team as an AI / Machine Learning Specialist, where you'll be at the forefront of transforming messy GTM data into actionable insights. With a strong emphasis on data quality and a collaborative work culture, you'll have the opportunity to shape the team's direction while enjoying generous benefits like 25 days of holiday. This role not only offers a chance for significant professional growth but also allows you to make a meaningful impact in a dynamic environment that values innovation and excellence.
StudySmarter Expert Advice🤫
We think this is how you could land AI / Machine Learning Specialist in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can get your foot in the door.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to data-heavy systems. We want to see how you tackle messy data and build solutions that work.
✨Tip Number 3
Prepare for the interview by diving deep into our tech stack. Familiarise yourself with Salesforce, HubSpot, and other tools we use. We love candidates who come in ready to discuss how they can improve our data pipelines.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, we’re always on the lookout for passionate individuals who are eager to join our team and tackle the challenges of GTM data.
We think you need these skills to ace AI / Machine Learning Specialist in London
Some tips for your application 🫡
Show Us Your Data Passion:When you're writing your application, make sure to highlight your love for data! We want to see how you've tackled messy data in the past and what strategies you've used to turn chaos into clarity. Share specific examples that showcase your experience with data-heavy systems.
Be Clear and Concise:We appreciate a straightforward approach. Keep your application clear and to the point. Avoid jargon unless it’s necessary, and make sure your skills and experiences shine through without fluff. We want to understand your journey and how it relates to the role!
Tailor Your Application:Don’t just send a generic application our way! Tailor your CV and cover letter to reflect the specifics of the AI/Machine Learning Specialist role. Mention your experience with the tools and systems we use, like Salesforce and HubSpot, to show you’re the perfect fit for our team.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you don’t miss out on any important details about the role. Plus, it shows you’re keen on joining the StudySmarter family!
How to prepare for a job interview at Stealth AI Company
✨Know Your Data Inside Out
Before the interview, make sure you can confidently discuss your experience with data-heavy systems. Be ready to walk them through a specific project where you tackled messy data and how you ensured data quality. This will show that you understand the core challenges of the role.
✨Showcase Your Engineering Mindset
Prepare to explain your thought process when building data pipelines or agent systems. Highlight your decision-making on when to use different retrieval methods like BM25 or embeddings. This will demonstrate your technical depth and ability to make informed choices in production.
✨Emphasise Team Culture and Leadership
Since you'll be shaping the team's culture, think about how you've contributed to team dynamics in the past. Share examples of how you've prioritised data quality and collaboration, and be ready to discuss how you plan to lead and mentor the next engineer you hire.
✨Prepare for Technical Questions
Expect to dive deep into technical discussions, especially around data ingestion, schema modelling, and hybrid search. Brush up on your Python or Go skills, as well as any relevant frameworks or tools you've used. Being able to answer these questions confidently will set you apart.
