AI Engineer | Build AI That Moves Beyond the Lab

A lot of AI roles today revolve around ad tech, recommendation engines, or abstract research that never sees real-world deployment.

This role is different.

You’ll be working on machine learning and deep learning applied to complex real-world physiological data, helping develop intelligent algorithms that ultimately become part of real systems used globally.

If you enjoy working with large datasets, time-series signals, and the challenge of turning research-grade models into robust production solutions, this role will likely interest you.

Why Engineers Like Working Here

Engineers who join this team tend to stay for a reason.

• You work across the entire product lifecycle from concept through to validation and deployment.
• The work combines algorithm development, software engineering, and systems integration.
• You collaborate with engineers, researchers, and domain specialists.
• The organisation has decades of engineering history building advanced technologies.
• There is room to grow either deeper technically or toward product leadership depending on your interests.
• The technology developed here ultimately supports real-world diagnostic and monitoring solutions.

You’ll also gain exposure beyond pure AI, including:

• signal processing
• systems integration
• algorithm development
• verification and validation
• design for manufacture
• regulatory considerations in product development

It’s the kind of role where you see how algorithms move from concept to deployed product.

What You’ll Be Doing

You’ll contribute to the development and optimisation of machine learning and deep learning models for physiological signal analysis.

Your work will include:

• Developing machine learning and deep learning models for time-series signal analysis
• Designing signal processing pipelines, including preprocessing, artefact detection and feature extraction
• Training and validating models using large real-world datasets
• Evaluating model performance and improving reliability and reproducibility
• Supporting the integration of algorithms into production software platforms
• Collaborating with multidisciplinary teams to ensure models work within practical systems
• Maintaining well-structured code and technical documentation
• Testing, debugging and improving algorithm performance
• Contributing to project deliverables and timelines

What You’ll Need

Strong fundamentals matter more than ticking every box.

• Degree in Artificial Intelligence, Computer Science, Biomedical Engineering, Electrical Engineering, or similar
• Strong experience with Python and modern ML frameworks such as PyTorch, TensorFlow, or Keras
• Solid understanding of machine learning and deep learning principles
• Experience with architectures such as CNNs, RNNs or related models for time-series analysis
• Experience with data preprocessing, feature engineering, and model evaluation
• Understanding of statistics, validation techniques and ML performance metrics
• Experience working with signal processing or time-series datasets

Nice to have:

• Experience working with large-scale datasets
• Exposure to cloud-based machine learning platforms
• Experience deploying ML models into production environments
• Experience working within structured engineering or regulated development environments

You’ll fit well if you are analytical, detail-oriented, and comfortable working in multidisciplinary engineering teams.

Important

This is not a purely research-driven role.

The algorithms developed here ultimately need to work reliably inside real products, which means balancing innovation with engineering discipline.

If you enjoy the challenge of turning AI models into robust production-ready solutions, this role will suit you.

Interested?

If this sounds like the kind of engineering problem you enjoy solving, reach out for a confidential conversation.

Phone: 0485991211
Email: simon@runtimerec.com

Even if you're just exploring options, feel free to get in touch.

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