Founding Machine Learning Engineer

A1; Bournemouth; On-site; GBP 60,000 - 90,000; Full time

Job summary

A cutting-edge AI organization in Bournemouth seeks a founding technical role focused on shaping the core technical direction of their new consumer AI application. Responsibilities include building end-to-end training pipelines, designing new model architectures, and creating scalable inference systems. This role offers the autonomy to explore frontier models and a chance to make a significant impact within a small, talented team.

Qualifications

• Experience with end-to-end machine learning pipelines.
• Proficiency in model selection and training strategies.
• Knowledge of scalable deployment architectures.

Responsibilities

• 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.

Skills

Machine Learning

Model Architecture Design

Scalable System Design

Tools

TensorRT-LLM

DeepSpeed

Job description

Job Description

About A1

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