AI/ML Researcher
Role details
Job location
Tech stack
Job description
We are looking for an AI/ML researcher to join the Knowledge and Semantics team within our Data and Decision Support Capability
You will have the opportunity to work with colleagues across the capability in multi-disciplinary teams and to work on a wide range AI topics for customers across the defence, security and commercial sectors as well as on internal BAE Systems AI programmes. You will also have the opportunity to maintain strong links with academic partners and SME partners as well as to grow technical research areas of interest to you.
You will have strong experience of working in projects on topics such as NLP, LLM applications/approaches (e.g. Agentic AI), knowledge graphs and/or graph machine learning and with a vision on how to develop solutions for practical applications of ML in these domains. You should have existing skills in Machine Learning (ML), will need to be a proficient programmer in Python, with extensive experience in the use of libraries, tools (docker, git) and frameworks (e.g. LangChain/LangGraph or similar) to support efficient development.
Typical Responsibilities:
- Propose and lead novel research in given topic areas.
- Lead technical delivery of small project teams. Prepare and deliver technical reports, technical proposals and supporting material.
- Develop prototypes and proof of concept demonstrators.
- Take ownership of tasks in projects and deliver to challenging standards.
- Effectively present results to both technical and non-technical audiences.
Requirements
- You will have a PhD in a relevant topic.
- Experience in software development in Python
- Experience with at least one ML framework: TensorFlow, Pytorch.
- Experience working with LLMs, knowledge graphs, or graph machine learning
- Of particular interest are candidates with the following experience (evidenced by a track record of publications, industry experience, open-source available code or equivalent academic work):
- Natural Language Processing, including Information extraction, text-mining and entity linking. Experience with modern (e.g. transformer-based) NLP models is desirable but not essential.
- Application of LLMs to Defence problems.
- The taxonomy of Graph Machine Learning tasks and experience in using graph ML in applied or foundational settings.
- Graph-structured data, designing and utilising relational and graph databases, and knowledge of graph algorithms.
Desirable Knowledge, Skills and Experience:
- Familiarity with knowledge representation, ontology design and semantic or LLM based reasoning
- Experience with one or more graph machine learning packages (PyTorch-Geometric, PyKeen etc.) and knowledge graph toolkits (Neo4j)