At a Glance
- Tasks: Own the ML layer, building systems that run at scale and improve over time.
- Company: Fast-moving AI startup focused on real-world applications.
- Benefits: Remote work, competitive salary, and a chance to shape impactful products.
- Other info: Collaborate closely with research and engineering leaders for significant career growth.
- Why this job: Make a real difference in AI by creating systems that users rely on daily.
- Qualifications: Deep understanding of ML, experience with large-scale models, and coding discipline.
The predicted salary is between 80000 - 98000 £ per year.
Most AI 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.
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:
- 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
Your skills and 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
- Multimodal or diffusion model background
- Large-scale data processing: Arrow, Spark, Ray
Why join
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.
Senior Machine Learning Engineer employer: Digital Waffle
As a Senior Machine Learning Engineer at our innovative company, you'll be part of a dynamic team that prioritises genuine impact over bureaucracy. We offer a collaborative work culture where your contributions directly shape the product, alongside opportunities for professional growth in a fast-paced environment. Enjoy the flexibility of remote work in the UK while engaging with cutting-edge technologies and making a real difference in users' lives.
