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- Tasks: Join us as a junior AI engineer, building innovative data pipelines and enhancing our knowledge graph platform.
- Company: Be part of a cutting-edge tech company at the forefront of AI development.
- Benefits: Enjoy private healthcare, wellness perks, and exciting team socials.
- Other info: Join a vibrant community of top talent and access exclusive events and networking opportunities.
- Why this job: Work directly with industry leaders and make a real impact in AI technology.
- Qualifications: Strong skills in Python or JavaScript, with a passion for AI and data engineering.
The predicted salary is between 40000 - 50000 £ per year.
What we're Building: Frontier models now score above 170 on IQ tests. Reasoning isn't the bottleneck. Context is. The context layer sits between an enterprise's siloed data and the agents that need to act on it. Stuff the context window and you trade quality for cost and latency. Use naive RAG and retrieval breaks the moment the question gets interesting. This gates most enterprise AI deployments we've seen, across private capital, professional services, edtech, and industrial data. 60x solves this. We built AI Brain, a knowledge graph platform engineered backwards from the agentic retrieval problem. Primary entity consolidation, chunk-level provenance, scheduled enrichment, Cypher queries. Agents retrieve what they need and the surrounding context, no bloat, no hope-and-pray. We run a Palantir model for workflows. The platform sits at the centre. Forward-deployed engineers wrap it around enterprise workflows we've templated. We retain each customisation as IP and feed it back into the platform, so each deployment gets faster, margins improve, and the moat widens. Same shape as Palantir's, different domain. We're at the start. Clients include private capital firms, edtech, automotive data, professional services, and a growing list of global consultancies evaluating us against their internal GPT deployments. In the last two weeks, we shipped a redesigned ingestion pipeline, primary entity extraction with auto-enrichment, and an end-to-end SP500 demo across 500 companies. We move at this pace as a default.
The Role: You'll be our junior engineer, working directly with the CTO (Exited Robotics Founder) and the senior engineering team on the parts of the platform that decide whether the context layer delivers. Ingestion and connectors. SharePoint, Google Drive, Gmail, DealCloud, and the next source on the list. Some clients hand us 400k+ files at 150+ GB and expect it to Just Work. You'll build the pipes and harden them. Knowledge graph internals. Primary entity consolidation, edge criteria, enrichment agents that decide when to call web search vs. internal tools, and the Cypher/Apache AGE query layer underneath. Agent infrastructure. LangGraph pipelines, Pydantic-typed state, prompt caching, the eval harness that keeps it honest. Product surface. The Next.js app where the graph, the reports, and the chat all meet the user. You won't be boxed into a single layer. By month three we'd expect you to have shipped real work in both the Python backend and the TypeScript frontend, and to have opinions about both.
Our Stack:
- Frontend: Next.js (App Router), TypeScript, Tailwind, shadcn, deployed on Vercel
- Backend: FastAPI, Python 3.12, Pydantic everywhere
- Agents: LangGraph, Claude via Vertex AI, Gemini for cheap/fast tagging work
- Data: Postgres + Apache AGE (graph), moving toward AlloyDB Omni on GKE where it fits
- Infra: GCP - GKE, Cloud Run, Cloud SQL, Vertex AI, KMS
- Tooling: pnpm, Husky commit hooks (ruff, eslint, prettier, typecheck, test build, and an agentic check that fixes what it finds), Linear for issues, Claude Code as a daily driver
We are opinionated about code quality and use AI coding agents hard. If pairing with Claude Code all day sounds uncomfortable, we're probably an odd fit.
Beyond the Role: The community. 60x sits at the centre of Unicorn Mafia, the invite-only builder community we run. ~1,100 members, invite only and tightening, maths olympiad winners, hackathon elite and the best founders across London, San Francisco, New York and Europe. Day one, you're in. Events free, sponsored international trips paid for. NY trips, hackathon weekends. Rooftop parties where the other guests are co-founders of major AI companies. We hand-pick who sits in the office to keep talent density high. Engineers from outside 60x show up because the room is worth being in. Most companies fly people in to get access to a room like this. Yours is at your desk.
The lifestyle: We look after our team and we socialise together. Private healthcare and a wider wellness benefits package. Sauna and cold plunge sessions, recovery and team time built in. Team socials, dinners, off-sites, and the natural overflow from UM events. An environment for people who want to do the best work of their lives without burning out.
Beyond the role: Strong fundamentals in at least one of Python, JavaScript or TypeScript. Something shipped, a project, a dissertation, an open-source contribution, a hackathon win, where you can walk us through architectural choices and what you'd do differently. Comfort in an agent-native workflow. You write the spec, the agent writes the first draft, you review it. If you've never done this, prove you'll pick it up fast. Interest in knowledge graphs, retrieval systems, agent orchestration, or enterprise data engineering. The taste and temperament to push back on a bad idea, including ours.
You do not need: A CS degree. Years of experience. To already know LangGraph, Apache AGE, or any specific framework in our stack.
AI Engineer employer: 60x
Contact Detail:
60x Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI Engineer
✨Tip Number 1
Network like a pro! Get out there and connect with people in the AI space. Attend meetups, webinars, or even just grab a coffee with someone in the industry. You never know who might have a lead on your dream job!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to Python, TypeScript, or knowledge graphs. Having something tangible to share can really set you apart from the crowd.
✨Tip Number 3
Don’t be shy about reaching out directly! If you see a role that excites you, drop a message to the hiring manager or team lead. Express your enthusiasm and ask any questions you might have. It shows initiative and can make a lasting impression.
✨Tip Number 4
Keep learning and adapting! Stay updated on the latest trends in AI and data engineering. Join online courses or communities where you can learn from others. This not only boosts your skills but also shows potential employers that you're committed to growth.
We think you need these skills to ace AI Engineer
Some tips for your application 🫡
Show Your Passion for AI: When you're writing your application, let your enthusiasm for AI and technology shine through. We want to see that you’re genuinely excited about the role and the impact you can make with us at 60x.
Tailor Your Application: Make sure to customise your application to highlight your relevant skills and experiences. Mention specific projects or technologies you've worked with that align with our stack, like Python or TypeScript, to show you’re a great fit.
Be Clear and Concise: Keep your application straightforward and to the point. We appreciate clarity, so avoid jargon and fluff. Focus on what you’ve done, what you can bring to the table, and why you want to join our team.
Apply Through Our Website: Don’t forget to submit your application through our website! It’s the best way for us to keep track of your application and ensure it gets the attention it deserves. We can’t wait to hear from you!
How to prepare for a job interview at 60x
✨Know Your Tech Stack
Familiarise yourself with the technologies mentioned in the job description, especially Python, TypeScript, and FastAPI. Be ready to discuss how you've used similar tools in past projects or how you would approach learning them quickly.
✨Showcase Your Projects
Prepare to talk about any projects you've shipped, whether it's a personal project, a hackathon win, or an open-source contribution. Highlight your architectural choices and be honest about what you would do differently next time.
✨Understand the Context Layer
Dive deep into the concept of context layers and knowledge graphs. Be prepared to discuss how they impact AI deployments and share your thoughts on improving data retrieval systems. This shows you're not just technically savvy but also understand the bigger picture.
✨Be Ready for Collaboration
Since you'll be working closely with the CTO and senior engineers, demonstrate your ability to collaborate effectively. Share examples of how you've worked in teams before, especially in environments that value code quality and feedback.