Machine Learning Engineer​/AI Research Engineer

We're looking for a Machine Learning Engineer / AI Research Engineer to join our small core team. This is a hands-on role sitting at the intersection of modern machine learning and quantum computing — with two complementary directions.

• ML for quantum — using neural networks and learning-based methods to accelerate quantum computation (e.g. circuit optimisation and compilation, error mitigation, calibration, variational-parameter optimisation, and surrogate modelling of quantum processes).
• ML on/with quantum — building and adapting classical AI methods, including neural networks and large language models, so they can benefit from quantum hardware and algorithms.

You don't need to be a world expert in either quantum or LLMs — but you should be genuinely comfortable with neural networks and excited to grow into the rest.

What the role looks like

• You'll work across the full lifecycle — research, design, coding, evaluation, and iteration.
• You'll design, train, and evaluate neural networks for both of the directions above.
• You'll help train, fine-tune, host, and serve models — including LLMs. Some of this you'll already know; parts you'll pick up on the job.
• You'll turn recent papers into working code, and working code into product.
• You'll collaborate closely with our quantum scientists, our CTO, and leading researchers at the University of Cambridge.
• You'll take real ownership of projects in a fast-moving, high-impact field.

This is a small, highly motivated team in central Cambridge. The environment is collaborative, relaxed, and academic — freedom to think creatively and work independently. Innovation gets celebrated. Achievements get rewarded.

What we're looking for

• Strong Python, with good software-engineering habits (Git, testing, reproducible experiments).
• Solid grounding in machine learning / deep-learning fundamentals — you can build, train, and debug neural networks from scratch in a modern framework (PyTorch, JAX, or similar).
• Demonstrable hands-on experience: research projects, publications, internships, open-source, or production work.
• Comfortable reading ML papers and reimplementing them.
• A fast learner who's happy with ambiguity and wants ownership.

Desirable (or willing to learn)

• Experience with LLMs: fine-tuning (LoRA / PEFT / full), serving and hosting (e.g. vLLM, TGI, Ollama), and distributed or multi-GPU training.
• Familiarity with GPUs, cloud, and containers (Docker, etc.).
• Background in optimisation, Bayesian methods, reinforcement learning, or time-series modelling.
• Exposure to quantum computing or quantum frameworks (Qiskit, Cirq, Penny Lane) — not required, we'll happily teach it.