Senior Machine Learning Engineer (Ecommerce)

TL;DR

Senior Machine Learning Engineer (Ecommerce): Building and optimizing large-scale machine learning systems for search and recommendations with an accent on deep learning, distributed training, and production-grade MLOps. Focus on transforming complex models into scalable, reliable systems that directly influence the customer shopping journey.

Location: Must be based in London, UK (Hybrid role at HQ in Camden).

Company

hirify.global is a global online fashion retailer dedicated to providing customers with the confidence to express their individuality through technology-driven shopping experiences.

What you will do

• Design, build, and improve machine learning systems powering search, ranking, and recommendation experiences.
• Collaborate with applied scientists and engineers to deploy models that deliver measurable commercial value.
• Develop and maintain batch and real-time machine learning models in production environments.
• Continuously improve codebase, engineering practices, and technical standards across the ML community.
• Mentor and support other engineers through technical collaboration and knowledge sharing.

Requirements

• Must be based in London, UK or able to work from the London office.
• Proven experience applying machine learning and deep learning techniques in production environments.
• Experience with deep learning frameworks and distributed computing technologies for large-scale models.
• Strong understanding of software engineering principles, development lifecycles, and MLOps practices.
• Experience with distributed training infrastructure, GPU-based environments, and parallelization.
• Strong communication skills with the ability to work effectively across cross-functional teams.

Culture & Benefits

• Competitive salary with a discretionary bonus scheme.
• Private medical care scheme and pension contributions matched up to 5%.
• 25 days annual leave plus an extra celebratory day.
• Exclusive employee discount for you and a nominated friend or family member.
• Flexible benefits allowance and access to personalized learning opportunities.
• Summer hours with early finishes on Fridays during summer months.