Senior​/Software Engineer, AI

Location: San Francisco, hybrid

Employment Type: Full-time

We’re working with a fast-growing, well-funded B2B SaaS company building AI-native software for complex, high-value professional workflows.

AI is becoming a core part of the product, and the engineering team is building the software and platform capabilities needed to make increasingly capable AI systems reliable in production.

The team is looking for experienced software engineers who have moved deeply into applied AI. The strongest candidates will have a strong foundation in backend or platform engineering and have since taken meaningful ownership of production LLM, agent, retrieval, or evaluation systems.

• Design and build production AI systems used directly within the company’s core product
• Own systems end-to-end, from architecture and implementation through deployment, monitoring, and iteration
• Build infrastructure for LLM agents, retrieval, RAG, evaluations, observability, and workflow orchestration
• Build evaluation and feedback systems
• Make architectural decisions around models, retrieval approaches, orchestration patterns, and system tradeoffs
• Work closely with product, design, and domain experts to turn ambiguous problems into reliable software

• Strong software engineering experience, ideally in backend, platform, infrastructure, or distributed systems
• A track record of owning technically difficult systems and shipping them into production
• Recent experience building production systems involving LLMs, AI agents, RAG/retrieval, tool calling, or evaluation
• Experience in a high-growth Seed–Series C product company is strongly preferred
• Comfortable operating in a high-ownership environment where engineers are expected to solve ambiguous problems independently

You’ll join at a point where the company already has strong funding, real customers, and an established product, while AI is becoming increasingly central to the platform.

Rather than working on isolated AI experiments, you’ll have significant ownership over the systems that determine whether AI actually works reliably for customers: orchestration, retrieval, evaluation, observability, backend architecture, and production reliability.