About The Company
A VC-backed AI/ML startup in West London building a novel foundation model for fully automated, unsupervised software delivery in embedded control systems. Early stage, high urgency, high transparency. They value directness over jargon and hands-on ownership over titles.
What You'll Actually Do
- Own a large-scale foundation model end to end, from research through production. This is 0 to 1 work, not maintaining someone else's architecture.
- Design and implement custom CUDA kernels where off-the-shelf libraries fall short.
- Architect and scale distributed training and inference pipelines on cloud infrastructure.
- Build and operate ML systems with strict production SLOs. Your models ship, not just train.
- Create internal tooling and infrastructure that accelerates the whole team's output.
- Work in a fast, ambiguous environment where you define what needs building next.
Core stack: CUDA, C++, Python, PyTorch, distributed training, GPU infrastructure, foundation models
Criteria I check in every CV (must-haves, only these matter)
- You've shipped a large-scale foundation model from 0 to 1 at a high-growth AI/ML startup or a top-tier research lab. Papers alone don't count.
- You've designed and implemented custom CUDA kernels to optimize model performance. This is non-negotiable.
- You have direct hands-on experience scaling distributed training or inference pipelines on cloud infrastructure (AWS, GCP, or Azure).
- You've owned an ML system with strict production SLOs or SLAs end to end, not just a research prototype.
What gets rejected immediately
- Academic or research-only experience without commercial production delivery.
- No proficiency in CUDA C/C++ or Python.
- No distributed training or inference experience at scale.
EU AI Act compliance
I use TeamTailor's built-in Co-Pilot to extract signals from CVs during the review stage. The decision to move a candidate forward is always made by a human person (me or the client). No automated decisions are made about your application.