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 systems including RAG pipelines, agentic workflows, and LLM-powered features. You will take work from prototype through production and ensure it can hold up in real customer environments.
Technical decision-making: Make principled decisions on when to prompt, when to fine-tune, and when to use a different tool entirely. You will explain these tradeoffs clearly so the team can move quickly without sacrificing quality.
Model evaluation: Instrument and evaluate model outputs rigorously by defining evaluation frameworks and catching hallucinations early. You will implement guardrails that can withstand real-world load and production use.
Productionize AI ownership: Own model deployment, monitoring, latency optimization, cost management, and reliability at scale. You will help ensure AI systems are observable, efficient, and dependable in production.
Full-stack product shipping: Contribute across the stack when needed because this team ships products, not just models. You will work across backend and frontend to get AI-powered experiences in front of users.
Product partnership: Partner closely with product and design to frame problems well before writing code. You will push back when the framing is wrong and help the team focus on what should be built, not just what can be built.
What they're looking for
- Research and tooling awareness: 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 apply them thoughtfully.
- Deep hands-on experience building and shipping LLM-powered systems, including: retrieval-augmented generation, tool use, and agent orchestration frameworks.
- Strong technical depth in system design, including choosing the right: architecture, identifying failure modes early, and making tradeoffs that hold up across the product lifecycle.
- Experience owning technical quality beyond your own features, including: setting standards, catching problems in review, and improving shared infrastructure and tooling.
