Back End Engineer (AI)

Role Description

As a Backend Engineer, you'll build the systems that make AI-native productivity experiences possible. You'll own the backend services that power AI interactions in production, ensuring they remain fast, reliable, and scalable as the platform grows.

• Designing and building backend services for AI-powered product features
• Developing inference pipelines and orchestration layers around LLMs
• Creating reliable APIs used by web, desktop, and mobile applications
• Optimizing latency, throughput, caching, batching, and streaming
• Building monitoring, logging, alerting, and observability across production services
• Debugging distributed systems and improving platform reliability
• Working closely with product, infrastructure, and AI engineers to continuously improve performance

Qualifications

• Strong backend engineering experience in production environments
• Experience building high-throughput, low-latency distributed systems
• Excellent Python skills (Node.js is a plus)
• Hands-on experience deploying and operating services on Kubernetes and Docker
• Experience with SQL and NoSQL databases
• Familiarity with modern AI infrastructure, including LLM inference, embeddings, or multimodal models
• A pragmatic engineering mindset focused on shipping reliable software

Requirements

• Experience with OpenAI, Anthropic, or open-source LLMs (nice to have)
• Experience building AI orchestration or agent workflows (nice to have)
• Knowledge of inference optimization techniques (nice to have)
• Experience operating AI services at scale (nice to have)

Tech Stack

• Python
• Node.js
• PyTorch
• OpenAI, Anthropic, open-source LLMs
• SQL & NoSQL
• Kubernetes
• Docker

Why this role is interesting

This isn't a traditional backend position. You'll be working on the infrastructure layer that connects cutting-edge AI models with real users. Your work will directly impact how quickly, reliably, and intelligently the product behaves in production.

If you enjoy solving problems around distributed systems, AI infrastructure, performance, and reliability—and want to build technology used by millions of people—this is an opportunity to work on one of the most technically challenging parts of an AI-native product.