Machine Learning Engineer (Ecommerce)

TL;DR

Machine Learning Engineer (Ecommerce): Building and maintaining production-grade machine learning systems for product discovery and personalisation with an accent on recommender systems and ranking models. Focus on deploying scalable models into batch and real-time environments and collaborating across engineering and science disciplines to deliver measurable commercial impact.

Location: London, UK

Company

hirify.global is a global online fashion retailer dedicated to providing customers with the confidence to express their individuality through technology and innovation.

What you will do

• Design, build, and maintain production-grade machine learning systems for personalisation and product discovery.
• Develop and improve recommender systems, ranking models, and customer-facing ML capabilities.
• Deploy models into batch and real-time environments, ensuring reliability and performance at scale.
• Collaborate with Applied Scientists and Engineers to transition models from experimentation to production.
• Monitor, evaluate, and iterate on models based on real-world customer behaviour and performance metrics.
• Contribute to engineering best practices, MLOps tooling, and shared machine learning platform capabilities.

Requirements

• Experience developing, deploying, or operating machine learning solutions in production environments.
• Familiarity with modern ML frameworks such as PyTorch, TensorFlow, or XGBoost.
• Understanding of software engineering fundamentals including version control, CI/CD, testing, and containerisation.
• Appreciation of MLOps practices and challenges of deploying systems at scale.
• Strong collaboration and communication skills for working across engineering, science, and product disciplines.
• Experience training models using GPUs or interest in distributed computing.

Culture & Benefits

• Private medical care scheme and pension contributions up to 5%.
• Exclusive employee discount for you and a nominated friend or family member.
• 25 days annual leave plus an extra celebratory day.
• Discretionary bonus scheme based on group performance.
• Access to personalised learning and development opportunities.
• Summer hours with early finishes on Fridays during summer months.