Cambridge, Cambridgeshire, United Kingdom Hybrid / WFH Options
Arm Limited
and Experience : Exposure to hardware design or verification processes (e.g., RTL, formal verification, simulation workflows). Familiarity with vector search, retrieval-augmentedgeneration (RAG), or multi-agent orchestration. In Return: At Arm, you'll work on AI challenges with practical impact across a global organization. You'll join a collaborative team that values deep … technical insight, clean engineering and creative problem-solving. Your contributions will influence how thousands of engineers at Arm design and verify the next generation of computing platforms-smarter, faster, and at scale. Accommodations at Arm At Arm, we want to build extraordinary teams. If you need an adjustment or an accommodation during the recruitment process, please email . More ❯
high 2:1, preferably in a technical subject Proficiency in Python and its ecosystem Active engagement with the AI space and proactive exploration of new AI technologies (Agents, MCP, RAG, Chain-of-Continuous-Thought, etc. More ❯
in the fastest-moving fields of current AI research, in particular in how to integrate a broad range of structured and unstructured multimodal information into AI systems (e.g. with RAG techniques), and get to immediately apply your results in highly visible Amazon products. If you are deeply familiar with LLMs, natural language processing and machine learning, this may be the More ❯
SINDy) Working knowledge of cell biology. Experience with Python, C, R or related scientific computing languages. Preferred Qualifications - Experience working with causal representation learning Experience with RAG (retrieval-augmentedgeneration) and GraphRAG a big plus Experience with building and deploying software on GitHub, PyPI, Anaconda Cloud, and Docker Hub, as well as use of More ❯
join our client in Pharma/Life Sciences. You'll be designing and building data infrastructure for an AI/ML solution and lead on prototyping and developing a RAG platform on AWS. You'll need: - Extensive AWS Data Engineering experience (S3, EC2, Glue, Lambada etc.) - NoSQL/MongoDB - Strong understanding of Data Modelling, ETL/ELT, DWH concepts - Docker …/EKS containerisation tooling - Experience building and maintaining pipelines for data workflows (CI/CD - RAG or LLM based data system experience If this sounds like you please reach out. More ❯
