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, PennyLane) — not required, we'll happily teach it. #J-18808-Ljbffr