Stott and May

Backend engineer in AI systems

In short

This is a backend engineering role at an AI-native startup backed by an initial $100M investment, building agent-first consumer applications. The job involves designing high-throughput backend systems, building AI inference pipelines integrating LLMs and multimodal models, and owning production reliability and performance. It requires strong backend experience in Python and/or Node.js, distributed systems and API development, exposure to production AI/ML or LLM inference systems, and familiarity with Docker, Kubernetes and SQL/NoSQL databases. The role is fully remote and requires at least 2 years of experience.

About
We're partnering with an ambitious AI venture, backed by an initial $100M investment, on a mission to transform how billions of people interact with technology. The company is building a new generation of AI-native, agent-first applications that proactively understand user needs, manage everyday tasks and execute complex workflows with minimal prompting. Combining substantial financial backing with the speed and autonomy of an early-stage startup, they're looking for an exceptional Backend Engineer - AI Systems.
Responsibilities
•Design and scale high-throughput, low-latency backend systems and APIs.
•Build AI inference pipelines and orchestration layers integrating LLMs, embeddings and multimodal models.
•Optimise latency, throughput and cost through caching, batching and streaming.
•Develop reliable infrastructure supporting complex, long-running AI workflows.
•Own system reliability, monitoring, observability and production performance.
•Work closely with AI/ML and product engineers to ship new capabilities.
Requirements
•Strong backend engineering experience building scalable, production-grade systems.
•Proficiency in Python and/or Node.js.
•Experience with distributed systems, high-throughput services and API development.
•Exposure to production AI/ML systems, LLM inference or model orchestration.
•Strong understanding of performance optimisation, reliability and production debugging.
•Experience with cloud infrastructure, Docker, Kubernetes and SQL/NoSQL databases.
Conditions
•$100M initial backing — significant resources to pursue an ambitious global vision.
•Build AI products for billions — help create the next generation of proactive, autonomous consumer applications.
•Genuine technical ownership — shape the infrastructure and architecture from an early stage.
•Cutting-edge engineering — solve complex challenges involving AI inference, distributed systems and large-scale orchestration.
•Small, world-class team — work alongside exceptional engineers with the pace and autonomy of a startup.

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