Staff Software Engineer (AI)

The Team + The Role

Our Engineering Team is focused on building AI Products for our product experience (PX) platform. We build from the ground up to explore, prototype, and ship AI-native experiences that change how software teams understand and serve their users. This is not an AI layer added to existing product; it is a deliberate bet on what product intelligence looks like next. The team operates with high autonomy, moves quickly, and builds products without clear precedents.

As a Staff Software Engineer (AI), you will sit at the intersection of deep technical capability and strong product judgment. You will design and build production-grade AI systems, including RAG pipelines, agentic workflows, and LLM-powered features, while making clear tradeoffs across prompting, fine-tuning, architecture, evaluation, and deployment. You will also partner closely with product, design, and engineering stakeholders to frame the right problems and communicate technical decisions clearly.

What this looks like day-to-day

• Applied AI systems: Design and build AI-native systems, including RAG pipelines, agentic workflows, and LLM-powered product features. Take ideas from prototype through production to support real users.
• Model strategy: Make principled decisions about when to prompt, when to fine-tune, and when to use a different technical approach entirely, communicating tradeoffs clearly to engineers and non-engineers.
• Evaluation and guardrails: Instrument and evaluate model outputs rigorously by defining evaluation frameworks and identifying hallucinations early. Implement guardrails that hold up under real-world usage and load.
• Productionize AI ownership: Own model deployment, monitoring, latency optimization, cost management, and reliability at scale. Ensure AI systems are observable, performant, and production-ready.
• Full-stack delivery: Contribute across the stack when needed to get complete AI products in front of users.
• Product partnership: Partner closely with product and design to frame problems well before implementation begins.
• Technical leadership: Stay current on research and tooling landscape (transformers, diffusion architectures, orchestration frameworks, emerging agent patterns) and raise the technical bar.

Must-haves

• Deep hands-on experience building and shipping LLM-powered systems, including retrieval-augmented generation, tool use, and agent orchestration frameworks.
• Demonstrated ability to set technical direction for AI systems across teams, establish architectural patterns, make foundational model strategy decisions, and raise the bar for AI engineering quality.
• Experience owning outcomes across team boundaries, including identifying capability gaps, driving alignment across engineering and product, and influencing organizational AI strategy.
• Strong command of model evaluation, including designing evaluation suites, reasoning about overfitting and bias-variance tradeoffs, and systematically detecting and mitigating hallucinations.
• Solid understanding of modern model architectures, including transformers and diffusion models, with the ability to make informed decisions about when and how to apply them.
• Production AI experience, including model deployment, monitoring pipelines, and latency, cost, and reliability optimization in live environments.
• Strong full-stack fundamentals and comfort working across backend and frontend systems to ship complete, user-facing AI products.
• Exceptional communication skills, with the ability to explain complex technical decisions clearly to engineers, product managers, and executives.
• Demonstrated product judgment and the ability to evaluate whether something should be built before determining how to build it.

Nice-to-haves

• Experience fine-tuning foundation models and a clear point of view on when fine-tuning outperforms prompting-based approaches.
• Familiarity with AI safety considerations, guardrail frameworks, and responsible deployment practices.
• Experience building agentic or multi-step reasoning systems using tools such as LangChain, LlamaIndex, or custom orchestration frameworks.
• Background in a SaaS or product analytics environment where user behavior data informs AI product design.
• Prior experience contributing to or launching a net-new team or product area.