Machine Learning Engineer | Defence Start-up

Machine Learning Engineer (Defence – hiring mid to senior levels | 2yrs+)

Newcastle (on-site)

Defence Tech Start-up

Machine Learning Engineer (Defence Tech)

A growing defence‑tech consultancy is seeking Machine Learning Engineers of various levels and disciplines to work on advanced AI solutions and/or design and implement machine learning systems, including LLM applications within secure environments.

You’ll genuinely be working alongside some of the best engineers/teams in the country, solving problems no one else has before.

They are deliberately setting a very high bar, hiring engineers from top universities who are among the strongest technically in their field. The work is very problem-solving focused, small teams tackling complex systems, often working on completely new challenges every few months.

People we’re hiring for and value

• Enjoy working on challenging systems
• Are friendly
• Truly understand problem-solving principles
• Are adaptable and articulate

Machine Learning Engineer Responsibilities may include

• Applying machine learning fundamentals and statistical techniques
• Working with LLMs and transformer architectures
• Evaluating model performance and optimising inference
• Developing solutions for constrained or secure environments
• Supporting explainability, safety, and robustness of AI systems
• Integrating AI/ML components into applications and workflows
• Designing and implementing retrieval-augmented generation systems
• Evaluating LLM performance and mitigating failure modes
• Testing and debugging non-deterministic systems
• Assessing when AI vs deterministic approaches are appropriate

Machine Learning Engineer Requirements (it would be useful if you have some of the following experience – not all is required)

• Deep understanding of machine learning fundamentals
• Strong mathematics and statistics knowledge
• LLM principles and transformer architectures
• LLM performance evaluation and inference optimisations
• Developing modules for edge, constrained or air-gapped environments would be a plus
• Explainable AI or AI safety/security would be a plus

Applied AI overlap

• Integrating AI components into applications and workflows
• LLM evaluation, failure modes, and mitigation strategies
• Testing and debugging non-deterministic systems
• Retrieval-Augmented Generation
• Understanding of the risks and limitations of AI and where statistical models or deterministic logic would be more appropriate
• Understanding of core AI/ML concepts such as LLM architectures, ML models, and statistical methods

Additional Criteria

• STEM Degree from a leading university (2:1 or 1st class)
• Eligibility for UK SC-level security clearance
• Experience in defence or highly regulated environments is advantageous, but not required

#J-18808-Ljbffr…