Founding Machine Learning Engineer

A1; Luton; On-site; GBP 70,000 - 90,000; Full time

Job summary

An innovative AI startup located in Luton, United Kingdom, is seeking a Founding Technical Role to shape the core direction in building consumer AI applications. You will have autonomy in designing systems and selecting models, responsible for architecting training and inference pipelines. The ideal candidate should have a robust background in Machine Learning and experience in building scalable systems. This position offers a unique opportunity to work with cutting-edge technologies and contribute to impactful AI solutions.

Qualifications

• Experience in building end-to-end training pipelines.
• Strong background in machine learning model design.
• Ability to implement state-of-the-art fine-tuning methods.

Responsibilities

• Build end-to-end training pipelines: data → training → eval → inference.
• Design new model architectures or adapt open-source frontier models.
• Architect scalable inference systems using vLLM, TensorRT-LLM, or DeepSpeed.

Skills

Machine Learning (ML)

Model training

Data architecture

Pipeline design

Scalable systems

Tools

LLaMA

Mistral

TensorRT

DeepSpeed

Job description

Overview

A1 is a self-funded, independent AI group, focused on building a new consumer AI application with global impact. We’re assembling a small, elite team of ML, engineering and product builders who want to work on meaningful, high-impact problems.

About The Role

You will shape the core technical direction of A1 - model selection, training strategy, infrastructure, and long-term architecture. This is a founding technical role: your decisions will define our model stack, our data strategy, and our product capabilities for years ahead.

You won’t just fine-tune models - you’ll design systems: training pipelines, evaluation frameworks, inference stacks, and scalable deployment architectures. You will have full autonomy to experiment with frontier models (LLaMA, Mistral, Qwen, Claude-compatible architectures) and build new approaches where existing ones fall short.

What You’ll be Doing

• Build end-to-end training pipelines: data → training → eval → inference
• Design new model architectures or adapt open-source frontier models
• Fine-tune models using state-of-the-art methods (LoRA/QLoRA, SFT, DPO, distillation)
• Architect scalable inference systems using vLLM / TensorRT-LLM / DeepSpeed
• Build data systems for high-quality synthetic and real-world training data