Description

Key Responsibilities 

• Design, develop, and deploy scalable machine learning systems that power demand forecasting and related AI products.
• Design and maintain reliable ML workflows across the model lifecycle, including data preparation, experimentation, training, evaluation, deployment, inference, monitoring, and continuous improvement.
• Build and optimise large-scale data processing and feature engineering pipelines using technologies such as PySpark and Databricks, enabling efficient preparation of training and inference datasets.
• Develop multimodal machine learning solutions that combine diverse data sources, including product images, text, structured metadata, and behavioural signals, to create rich representations for downstream AI applications.
• Train and productionise deep learning models using modern architectures such as Transformers, foundation models, and other representation learning approaches.
• Partner with Applied Scientists to translate new modelling approaches into reliable, scalable production systems.
• Improve the performance, reliability, scalability, and observability of ML systems operating in production.
• Drive engineering excellence through architecture discussions, code reviews, mentoring, and knowledge sharing.

• Significant experience designing and deploying machine learning systems in production environments.
• Strong software engineering skills in Python, with experience building maintainable, tested, and production-quality code.
• Strong experience with large-scale data processing using technologies such as PySpark and Databricks.
• Experience designing and building ML pipelines across the full lifecycle, from data preparation and model development through to deployment and monitoring.
• Experience developing deep learning models using frameworks such as PyTorch, TensorFlow, or similar.
• Experience with multimodal machine learning, representation learning, or embedding models, combining data sources such as images, text, structured metadata, or behavioural signals.
• Strong understanding of modern deep learning architectures, particularly Transformers, foundation models, multimodal learning, and representation learning techniques.
• Experience working with distributed computing, large datasets, and scalable model training or inference systems.
• Familiarity with cloud platforms and modern MLOps practices.
• Strong communication skills and the ability to collaborate effectively with scientists and engineers.
• A pragmatic mindset, balancing technical excellence with delivering business value

Key Skills

Education

Bachelor's degree