Backend Engineer, AI

We're looking for a

backend-focused engineer

to help build and scale the infrastructure behind AI-driven products. The role involves working across application services, model integration, and distributed systems, with a strong emphasis on building reliable production software.

You'll be responsible for turning AI capabilities into dependable backend services that can support real-world usage across multiple product surfaces.

Sounds great - what will I do

Develop and maintain backend services supporting AI-enabled applications.

Build service architectures and processing workflows around machine learning models.

Integrate and manage interactions with LLMs, embeddings, and other AI capabilities.

Improve system performance through techniques such as caching, batching, asynchronous processing, and streaming.

Establish and maintain effective monitoring, logging, alerting, and operational practices.

Troubleshoot complex issues across distributed services and production environments.

Work closely with engineering and AI/ML teams to bring new capabilities from development into production.

Sounds perfect to me, what specifics are you looking for

Strong software engineering fundamentals with solid backend development experience.

Experience building scalable services where performance and reliability are important.

Exposure to AI/ML systems, particularly LLM-based applications, inference workflows, embeddings, or multimodal technologies.

Comfortable working with distributed architectures and diagnosing issues in production.

Practical, hands-on approach to engineering with an emphasis on delivering and iterating quickly.

Ability to balance engineering quality, performance, scalability, and operational considerations.

What Success Looks Like

Backend services remain stable and performant as AI workloads grow.

AI capabilities can be exposed through well-designed, maintainable APIs and services.

Production issues are identified and resolved efficiently with minimal disruption.

System performance, scalability, and reliability improve continuously through measurement and iteration.

New AI capabilities can be integrated into the product without creating unnecessary operational complexity.

Technical proficiency across:

Python

Node.js

PyTorch

Commercial and open-source LLM platforms

SQL and NoSQL databases

Kubernetes

Docker

Cloud-based infrastructure and distributed services