The opportunity
Our Emerging 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.
What you'll do
Applied AI systems: Design and build AI-native systems, including RAG pipelines, agentic workflows, and LLM-powered product features. You will take ideas from prototype through production and ensure they can 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. You will explain those tradeoffs clearly to engineers and non-engineers.
Evaluation and guardrails: Instrument and evaluate model outputs rigorously by defining evaluation frameworks and identifying hallucinations early. You will 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. You will 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. This team ships products, not just models, and you will help close the gap between technical capability and user experience.
Product partnership: Partner closely with product and design to frame problems well before implementation begins. You will push back when the framing is wrong and help the team stay focused on what is worth building.
What they're looking for
- Technical leadership: Stay current on the research and tooling landscape, including transformers, diffusion architectures, orchestration frameworks, and emerging agent patterns. You will bring relevant advances back to the team and help raise the technical bar.
- 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 how a broader organization approaches AI.
