Social network you want to login/join with:
If you think you are the right match for the following opportunity, apply after reading the complete description.
Machine Learning Engineer – Founding Team (Computer Vision / GenAI), London Client:
Location:
London, United Kingdom
Job Category:
Other
-
EU work permit required:
Yes
Job Views:
4
Posted:
26.06.2025
Expiry Date:
10.08.2025
Job Description: A well-backed, early-stage startup is building a cutting-edge AI platform that transforms real-world visitor experiences at physical venues — from cultural institutions to entertainment destinations. Following successful pilots with major partners and recent pre-seed investment, we’re scaling up and looking for a Machine Learning Engineer to lead the development of our core AI systems.
What You’ll Work On: Computer Vision: Enable the system to recognise what users are viewing in real time using image embeddings, similarity search (e.g., CLIP, vector search), and traditional CV approaches (e.g., YOLO, MobileNet).
LLM & RAG Systems: Design and implement pipelines that support retrieval-augmented generation, internal AI tools, and scalable content delivery. Experience with vector databases, agent frameworks, or data workflows is highly relevant.
Deployment & MLOps: Own model deployment pipelines, including API-based serving, monitoring, and cloud infrastructure (AWS preferred, but others welcome). Bonus points for edge/offline deployment experience.
Work directly with the founder on roadmap decisions, help shape technical direction, and grow into a potential leadership role as the team expands.
About You: 2–5+ years of experience in ML/CV/GenAI
Proficiency in Python, ML frameworks, and cloud-based infrastructure
Product-focused mindset with a desire to shape early-stage tech
UK-based and open to occasional travel for testing and collaboration
Why Join: Build something novel at the intersection of CV and GenAI
Be part of the founding team with real influence and equity
Work remotely with the flexibility to grow your role as we scale
Backed by strong early traction and funding
#J-18808-Ljbffr