Head of Machine Learning

Digital Waffle; United Kingdom; On-site; GBP 120,000 - 180,000; Full time

Job summary

Digital Waffle is building a scalable ML infrastructure and seeks a hands-on leader to own the full ML lifecycle, from data to deployed models.

You will bridge research and production, guide a small team, and deliver robust, low-latency systems while improving models over time. The role emphasizes end‑to‑end pipelines, optimization, and collaboration with engineers across back-end and front-end teams.

Qualifications

• Strong track record leading ML teams and delivering production models.
• Deep learning expertise with transformer architectures.
• Hands-on experience with PyTorch or JAX and distributed training tools.
• Ability to design robust data pipelines and scalable inference systems.

Responsibilities

• Own end-to-end ML pipelines from data to deployment.
• Architect scalable, low-latency inference systems.
• Lead model fine-tuning, evaluation, and deployment updates.
• Collaborate with engineers to integrate ML into products.

Skills

Team leadership

Deep learning

Transformer architectures

PyTorch

JAX

Distributed training

Code quality

GPU optimization

Ambiguity handling

Production ML

Tools

DeepSpeed

FSDP

Megatron

ZeRO

Ray

Job description

Most ML products are wrappers. We're building the real thing, an agent that takes on genuine tasks for everyday users: running errands, managing workflows, holding context across long and complex conversations. Reliable by design, not by luck.

We're small, we move fast, and the ML layer is the product. We need someone to own it and lead the team.

The role

You'll bridge research and production, taking ideas and turning them into systems that run at scale, stay reliable, and get better over time. Full-stack ML ownership: from raw data to deployed model.

Day to day that looks like:

• Managing end-to-end pipelines across data, training, evaluation, and inference
• Adapting and fine-tuning models with modern techniques: LoRA, QLoRA, SFT, DPO, distillation
• Architecting inference systems that hold up under real latency and cost constraints
• Creating data pipelines that produce high-quality synthetic and real-world training data
• Running evaluation that goes beyond benchmarks: robustness, safety, bias, production behaviour
• Owning deployment: GPU optimisation, quantisation, memory efficiency, scaling
• Working directly with application engineers so ML integrates cleanly into backend, mobile, and desktop

Your skills and experience

• Previous team lead experience
• Deep understanding of deep learning and transformer architectures
• Proven experience training, fine-tuning, or shipping large-scale models in production
• Strong with at least one major ML framework (PyTorch, JAX) and quick to pick up others
• Familiar with distributed training and inference tooling: DeepSpeed, FSDP, Megatron, ZeRO, Ray
• Engineering discipline: code that's readable, robust, and maintainable
• Experience optimising for GPU constraints: quantisation, mixed precision, memory
• Comfortable taking ownership of ambiguous problems from zero to one
• Ships, iterates, learns from production

Nice to have

• LLM inference frameworks: vLLM, TensorRT-LLM, FasterTransformer
• RLHF: PPO, DPO, ORPO
• Open-source contributions to ML or systems libraries
• Scientific computing, compiler, or GPU kernel experience

At a big company, ML work gets absorbed into a machine. Here, your systems are the product. You'll work closely with research and engineering leadership, have real influence over how the architecture evolves, and see the direct impact of your work on users. If you want to build ML infrastructure that actually matters, this is it.