COMPANY OVERVIEW:

There are over 5 billion users using basic applications today such email, notes, tasks that are not AI-native. This company's mission is to build a proactive smart assistant for everyday users to bring intelligence to conversations, errands, organizing and workflows, with minimal prompting.

Their product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior. The objective is to help users complete tasks daily enjoyable with over ~90%* reduced time.

The best products today in the world were built by small, world class teams. This is a high talent density and hands-on team. They make decisions collectively, move at rapid speed, striking a balance between shipping high quality work and learning. Joining the team requires the ability to bring structure, exercise judgment, and execute independently. The goal is to put in hands of users a truly magical product.

POSITION OVERVIEW:

As a Backend Engineer, AI, you own the inference and orchestration layer that powers every AI interaction in the product. Your work sits between models and users, where latency, correctness, reliability, and cost directly impact real-world experience.

You will build and operate production systems that turn model capability into fast, stable, observable APIs used across mobile and desktop clients.

RESPONSIBILITIES:

• Build and operate backend systems that serve AI-powered features in production.
• Design inference pipelines, orchestration layers, and service boundaries around models.
• Own production concerns: monitoring, logging, alerting, and incident response.
• Optimize latency and throughput across inference, caching, batching, and streaming.

PREFERRED PROFILE:

• Strong backend engineering fundamentals in production environments.
• Experience running high-throughput, low-latency services.
• Familiarity with AI inference patterns (LLMs, embeddings, multimodal).
• Comfortable debugging distributed systems under load.
• Bias toward shipping and learning from production behavior.

TECH STACK:

• Python
• NodeJs
• Pytorch
• OpenAI / Anthropic / open-source LLMs
• SQl & noSQL
• Kubernetes
• Docker

 DESIRED OUTCOMES:

• Backend systems run reliably at scale, handling production AI traffic with low latency and high throughput.
• APIs are stable, clear, and support seamless integration with frontend and ML systems.
• Production incidents are quickly detected, diagnosed, and resolved, minimizing user impact.
• Iterative improvements based on real usage continuously increase system performance and reliability.

LOCATION: Remote

Job ID# 3635080

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