Data Science/AI ML Engineer

Role Description

We're a research-driven team pushing the boundaries of what's possible with Large Language Models. Our work sits at the intersection of applied ML research and production engineering — we're not just consuming off-the-shelf models, we're building our own. We're developing novel approaches to create specialised LLMs that deliver commercial-grade intelligence at a fraction of the cost of frontier API models.

We're looking for an AI/ML Engineer who gets excited about model internals — someone who thinks in terms of tensor operations, architecture design, and weight spaces rather than just prompt templates. You'll join a collaborative, diverse team where research ideas move quickly from paper to prototype to production, and where your contributions will directly shape the architecture of next-generation AI systems used by millions.

What You Will Do

• Contribute to the design and implementation of specialised Large Language Models, working with model weights, attention mechanisms, and efficient sparse architectures.
• Participate in the lifecycle from research spike through to deployable artefact — reading papers, prototyping in notebooks, building reusable Python libraries, and deploying on GPU-accelerated cloud infrastructure.
• Help build and maintain evaluation frameworks to compare model variants across intelligence, reasoning capability, and inference cost.
• Support and extend the cloud infrastructure (AWS CloudFormation, SageMaker, S3, GPU instances) that powers our experimentation and model serving.
• Engage with the open-source ML ecosystem, leveraging established toolkits for model development and evaluation.
• Actively participate in design discussions, contribute ideas to the team's research agenda, and provide guidance to more junior team members where appropriate.

Qualifications

• Degree in Computer Science, Machine Learning, Mathematics, or a related quantitative field (or equivalent hands-on experience).
• 3+ years in ML engineering, with at least 1 year working directly with LLMs or deep learning model architectures.
• Good understanding of transformer architectures, attention mechanisms, and model internals.
• Solid proficiency in Python with hands-on experience in PyTorch.
• Familiarity with AWS services for ML workloads — SageMaker, EC2 GPU instances, and S3 for model storage.
• Clean code practices, Git workflows, CI/CD (Jenkins or similar), unit testing, and the ability to write well-structured Python code.

Requirements

• Exposure to sparse architectures, expert routing, conditional computation, or techniques for adapting and combining pre-trained models.
• Awareness of quantization, model compression, or distributed inference strategies for running large models efficiently.
• Experience with CloudFormation, Terraform, or similar tools for reproducible ML infrastructure deployments.
• Familiarity with the contact centre industry, conversational AI, or agentic AI systems is a plus but not essential.
• An awkward sense of humour and a genuine love for digging into model internals.

Benefits

• Work on genuinely novel research — new approaches to building LLMs that could reshape how the industry thinks about model development and cost optimisation.
• Clear paths toward technical leadership, the freedom to explore research directions, and a team culture built on mutual respect.
• Remote-friendly, with flexible working arrangements and a genuine commitment to work-life balance.