Senior Backend Engineer: Machine Learning Infrastructure

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Senior Backend Engineer: Machine Learning Infrastructure based in United Kingdom.

As a Senior Backend Engineer, you’ll help build the infrastructure that enables machine learning teams to develop, deploy, and operate models reliably at scale.

You’ll own critical backend services, data pipelines, and platform capabilities supporting workloads ranging from traditional machine learning to LLMs.

The role combines distributed systems engineering, cloud infrastructure, API development, observability, and platform design.

You’ll work closely with ML and product teams to understand their needs and turn them into reusable, self-service infrastructure.

You’ll have significant autonomy to make technical decisions, evaluate trade-offs, and take solutions from design through continuous improvement.

The environment is remote-first, collaborative, and strongly oriented toward ownership, thoughtful engineering, and modern AI-assisted development.

This is an opportunity to have a direct impact on how teams build and operate machine learning products at scale.

Accountabilities

• Design, build, deploy, and operate high-load distributed backend services and APIs that support machine learning infrastructure.
• Take end-to-end ownership of core ML services and associated data pipelines, from system design and implementation through deployment, observability, maintenance, and continuous improvement.
• Build reliable, scalable, and reusable infrastructure components that make machine learning workloads easier for product and ML teams to run and operate.
• Partner closely with ML and product engineers to understand their requirements and translate them into effective platform capabilities and services.
• Make and communicate technical decisions by evaluating architectural options, trade-offs, scalability, reliability, and operational requirements.
• Maintain a high standard for service reliability, performance, observability, and maintainability.
• Proactively identify technical and operational problems and take ownership of resolving them rather than allowing issues to remain unaddressed.
• Use modern AI-assisted development tools thoughtfully while maintaining strong ownership of system design, engineering decisions, and code quality.
• Contribute to a collaborative engineering culture by sharing knowledge, supporting teammates, and helping others solve technical challenges.