Description
The role
We are looking for an early-career engineer who enjoys understanding how up-to-date AI systems actually work.
You will work across model experimentation, inference, evaluation, deployment, and ML systems performance. This is a hands-on engineering role: you will build prototypes, run experiments, investigate failures, profile systems, and turn promising ideas into working implementations.

Responsibilities
What you will do
- Experiment with LLMs and multimodal models.
- Build prototypes and production-quality components in Python and PyTorch.
- Run and optimise model inference using tools such as vLLM.
- Measure and improve latency, throughput, batching, caching, GPU utilisation, and memory usage.
- Build evaluation frameworks to understand model quality and behaviour.
- Work with embeddings, retrieval, reranking, structured generation, and tool-using/agentic systems.
- Deploy model-backed services and troubleshoot them in realistic workloads.
- Read relevant research papers and reproduce or test promising ideas.




- Design controlled experiments and analyse failures across data, model, software, and infrastructure.
- Document findings clearly, including what worked, what failed, and why.

Qualifications
Core requirements
- Strong Python skills.
- Working knowledge of PyTorch and modern neural-network architectures.
- Understanding of transformers, tokenisation, embeddings, attention, sampling, and decoding.
- Ability to write maintainable software beyond notebooks.
- Comfortable working with Linux, Git, Docker, APIs, and basic cloud infrastructure.
- Strong analytical and debugging skills.

Useful ML systems knowledge
You should understand, or be motivated to learn:
- model serving and inference;
- vLLM or similar runtimes;
- continuous batching and KV caching;
- quantization;
- throughput vs latency trade-offs;
- GPU memory constraints;
- mixed precision and device placement;
- profiling and out-of-memory debugging;
- structured/constrained generati

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