Senior ML Engineer
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Job Summary
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 youll actually do
Own a large-scale foundation model end to end from research through production. This is 0 to 1 work not maintaining someone elses 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 teams 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)
Youve 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 dont count.
Youve 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).
Youve 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 TeamTailors 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.
