Senior Product Engineer

• This role sits at the intersection of AI, product, and full-stack engineering: you will take ideas from prototype all the way to stable, observable features in production.
• Design and ship AI-powered features such as assisted requirement drafting, consistency checks, impact analysis, and intelligent suggestions for systems and domain engineers.
• Build agentic workflows that help “agentic systems engineers” and “agentic domain engineers” explore designs, simulate changes, and validate requirements.
• Evaluate and integrate language models and related tooling, optimizing for reliability, latency, cost, and debuggability in production.
• Build and maintain the surrounding infrastructure: data pipelines, evaluation harnesses, prompt and model management, observability, and safety/guardrails.
• Work across the stack—from backend integrations and APIs to simple UI hooks—to deliver complete AI features, not just model endpoints.
• Partner with product and customers to identify high-value workflows, run experiments, and iterate quickly based on usage.
• 5+ years developing production software, including designing, testing, and operating services at scale in a cloud environment.
• Hands-on experience with modern LLM providers and tooling (e.g., OpenAI, Anthropic, Hugging Face, vector stores, RAG patterns).
• Familiarity with prompt design, retrieval-augmented systems, evaluation methods, and safety/guardrail approaches.
• Ability to reason about tradeoffs between different models, architectures, and deployment patterns and make pragmatic decisions.
• Comfortable working in a high-ownership, fast-paced environment where experiments and iteration are the norm.
• TypeScript/Node.js and Python for AI and backend services.
• Modern LLM APIs and orchestration libraries for building agentic workflows.
• Postgres and other managed cloud services for data and state.
• Speed over everything: prototype AI workflows quickly, then harden what works.
• Own, downscope, ship, iterate: one clear owner per feature, from prototype to production.
• Fundamentals done well: evaluation, observability, and safety are part of the first version, not an afterthought.
• Competitive salary and meaningful equity.
• Flexible time off and support for experimentation, learning, and staying current with the AI ecosystem.

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