Backend / Fullstack Engineer

San Francisco, California

Software Engineering

Permanent

Our client, an exciting AI-driven startup, is hiring a Backend/Full Stack Engineers to join the team in San Francisco. The successful candidate will join a tight-knit team of top engineers, pioneering the next generation of websites and online commerce powered by LLMs (Large Language Models).

Responsibilities

Design, build and maintain scalable web applications and backend systems that power AI-driven experiences.
Collaborate closely with product and design teams to rapidly iterate on 0?1 features and experiments.
Implement and optimise LLM integrations, retrieval systems and AI-powered workflows.
Build robust data pipelines, real-time data flows and distributed systems to support dynamic applications.
Ensure high-quality user experiences through careful attention to performance, reliability and usability.
Participate in architectural decisions, code reviews and knowledge sharing to raise the team’s technical bar.
Monitor and improve system observability, performance and security.
Stay ahead of AI, web and infrastructure trends to continuously improve our products and engineering practices.

Skillset

Proven experience taking products from 0?1, balancing speed, scalability and quality – whether as a founder or early-stage startup engineer.
Hands-on expertise with cutting-edge AI models, LLM retrieval systems or data-intensive applications, with a strong understanding of current and emerging capabilities.
Passionate about crafting high-quality user experiences and tackling the practical challenges of building AI-powered products.
Successfully shipped production LLMs or consumer-facing applications that users rely on and enjoy.
Built recommendation engines or critical data infrastructure that scale reliably.
Proficient with Postgres, ElasticSearch, AWS, and infrastructure-as-code (IaC) tools.
Familiar with modern stacks and languages such as Elixir, Go, TypeScript, NextJS, Fastify, Python and similar technologies.
Experienced in model-based evaluation, monitoring and observability systems to ensure product reliability.