Backend Engineer

About the Role

As a Backend Engineer – AI, you will own the inference and orchestration layer that powers AI interactions across our product. Your work sits directly between models and users, where latency, correctness, reliability, scalability, and cost have a direct impact on the user experience. You will build and operate production-grade systems that transform raw model capabilities into fast, reliable, and observable APIs for mobile and desktop clients.

Core Focus & Responsibilities

• Backend Engineering: Build, deploy, and operate production backend systems that power AI-driven product features.
• Pipeline & Architecture Design: Design inference pipelines, orchestration layers, and clear service boundaries around ML models and AI capabilities.
• Production Reliability: Own monitoring, logging, alerting, and incident response to maintain high levels of system availability and stability.
• Performance Optimization: Optimize latency, throughput, and resource utilization across inference, caching, batching, and streaming mechanisms.
• Scalability: Design systems that can efficiently handle growing AI workloads and production traffic while maintaining consistent performance.

What We’re Looking For

• Backend Fundamentals: Strong backend engineering fundamentals and experience building services for high-volume production environments.
• High-Performance Services: Proven experience building and operating high-throughput, low-latency backend services.
• AI Inference Patterns: Familiarity with AI inference architectures, including LLMs, embeddings, multimodal models, and model-serving workflows.
• Distributed Systems: Strong understanding of distributed systems and the ability to debug complex systems operating under production load.
• Production Ownership: Strong bias toward shipping, monitoring, and iterating quickly, with a willingness to learn directly from real-world production behavior.
• Engineering Judgment: Ability to make pragmatic technical decisions while balancing performance, reliability, scalability, and infrastructure costs.

Tech Stack

• Languages & Environments: Python, Node.js
• AI & ML Frameworks: PyTorch, OpenAI, Anthropic, and open-source LLMs
• Databases: SQL & NoSQL
• Infrastructure & Containerization: Docker, Kubernetes

Expected Outcomes

• Scalable Infrastructure: Build and operate backend systems that reliably handle production AI traffic at scale while maintaining low latency and high throughput.
• Clean API Boundaries: Establish stable, well-structured APIs that enable seamless integration between frontend clients, backend services, and ML systems.
• Rapid Incident Resolution: Detect, diagnose, and resolve production issues quickly to minimize downtime and user impact.
• Continuous Performance Gains: Continuously improve system performance, scalability, and reliability based on real-world usage data and production insights.

How We Work & Application Process

We are a small, world-class team with a high talent density. We operate at a rapid pace while balancing high-quality engineering with continuous learning, experimentation, and iteration.

• Interview Process: If there appears to be a mutual fit, we will schedule 3–4 interviews with members of the technical team, conducted virtually and/or on-site. We value transparency and efficiency and aim to make timely, well-informed decisions throughout the process.