Senior ML Engineer

WhyHireWrong?; Greater London; On-site; GBP 120,000 - 180,000; Full time

Job summary

WhyHireWrong? in London seeks an AI/ML前 engineer to drive end-to-end development of large-scale foundation models. You will design CUDA kernels, build scalable training and inference pipelines on cloud platforms, and own ML systems with strict production SLAs.

You will work in a fast, high-urgency environment, shipping models and creating internal tooling to accelerate the team’s output. Strong emphasis on hands-on ownership over titles."

Qualifications

• Shipped a large-scale foundation model from 0 to 1 at a high-growth AI/ML startup or top-tier research lab.
• Designed and implemented custom CUDA kernels to optimize model performance.
• Experience scaling distributed training or inference pipelines on cloud infrastructure (AWS, GCP, or Azure).
• Owned an ML system with production SLOs/SLAs end to end, not just a research prototype.

Responsibilities

• Own a large-scale foundation model end to end, from research through production.
• 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. 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.

Skills

Large-scale foundation model 0→1

CUDA kernel development

Distributed training/inference scaling

Production ML systems with SLOs

Tools

CUDA

C++

Python

PyTorch

AWS/GCP/Azure

Job description

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.