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# Senior ML Engineer

- **Company:** Digital Waffle
- **Location:** London, United Kingdom
- **Experience:** Expert
- **Salary:** £119,322
- **Contract:** Permanent contract
- **Skills:** Training Data, Big Data, Distributed Computing Environment, Performance Tuning, Raw Data, Scientific Computating, QLoRA, Pytorch, DeepSpeed, Large Language Models, Apache Spark, Deep Learning, Backend, Transformer Architectures, Free and Open-Source Software, TensorRT, Mixed Precision, Model Inference, Megatron, Data Pipelines
- **Published:** November 12, 2026
- **Apply:** https://www.adzuna.co.uk/jobs/details/5962347256

## About the Role

* 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
 * Multimodal or diffusion model background
 * Large-scale data processing: Arrow, Spark, Ray

## Description

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:

 * Building 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