ML Research Engineer

We are looking for an Applied AI Researcher / ML Research Engineer to join the AI Product team. This is a deployment-focused role, where the work goes beyond research, experimentation, or proof-of-concept development. The successful candidate will work on AI models and solutions that are expected to be deployed into real-world products. This role is suitable for someone with strong hands-on experience in neural network architectures, especially CNNs and Transformer-based models, and who enjoys solving practical AI problems with measurable business impact.

Responsibilities

• Design, train, fine-tune, evaluate, and improve deep learning models for real-world AI applications.

• Work with neural network architectures, including CNNs and Transformer-based models.

• Conduct experiments to validate model performance, reliability, and generalisation on unseen data.

• Select and apply appropriate loss functions, model architectures, and training strategies based on the problem being solved.

• Analyse model performance, identify weaknesses, and improve accuracy, robustness, and efficiency.

• Translate applied research into deployable AI solutions, not just prototypes or proof-of-concepts.

• Collaborate with product, engineering, and business teams to ensure AI solutions are practical, scalable, and production-ready.

• Deliver research and model development work with a strong focus on measurable real-world impact.

Requirements

• Bachelor's degree or higher in Computer Science, Engineering, Mathematics, Artificial Intelligence, Machine Learning, Data Science, or a related field. Master's degree or PHD preferred.

• Hands-on experience with neural network architectures, especially CNNs and/or Transformer-based models.

• Experience training, fine-tuning, evaluating, or improving deep learning models.

• Strong understanding of deep learning fundamentals, including model architectures, loss functions, optimisation methods, and training dynamics.

• Ability to evaluate how well a model generalises to unseen data.

• Hands-on experience with PyTorch and/or TensorFlow.

• Strong programming skills, especially in Python.

• Ability to explain technical model decisions clearly, including architecture choice, loss function selection, and performance trade-offs.

• Practical mindset with the ability to move applied research into deployment-ready solutions.

Preferred Qualifications

• Experience working on AI/ML solutions that have been deployed or prepared for production use.

• Experience with computer vision, NLP, large language models, vision-language models, or foundation models.

• Experience with model evaluation, benchmarking, performance optimisation, and experiment tracking.

• Familiarity with real-world deployment considerations such as reliability, scalability, latency, and efficiency.