We’re hiring a Applied AI Engineer to join a fast-moving, high-ownership team building next-generation AI assistant and productivity capabilities. This role blends LLM product engineering, evaluation science, hillclimbing, and internal tool building with the pace and creativity of a startup. You’ll work across the entire lifecycle of features from early prototypes to production-grade systems and help define how millions of users interact with AI.

LLM Feature & Agent DevelopmentDesign and ship LLM-powered assistant features, including conversational flows, agentic behaviors, retrieval pipelines, and multimodal interactions.Build prompt architectures, system instructions, and orchestration logic that ensure reliability, grounding, and personality consistency.Prototype new capabilities rapidly and iterate based on user signals and evaluation data.Evaluation, Hillclimbing & Quality SystemsBuild and maintain evaluation frameworks for correctness, safety, grounding, and UX quality.Run hillclimbing loops across prompts, models, and tool-use strategies to continuously improve assistant performance.Analyze failure modes, design mitigations, and drive systematic improvements across the stack.LLM Tooling & Internal InfrastructureDevelop internal tools for prompt experimentation, model comparison telemetry and debugging automated eval pipelinesCreate reusable frameworks that accelerate the entire AI org’s ability to ship high-quality assistant features.Applied ML & Product IntegrationIntegrate LLMs with product surfaces, APIs, and backend systems.Build lightweight ML components (ranking, classification, summarization, personalization) that enhance assistant intelligence.Collaborate with PM, design, and research to turn ambiguous ideas into polished user experiences.High-Velocity TeamworkOperate with startup-founder energy: bias for action, rapid iteration, and comfort with ambiguity.Work closely with researchers, engineers, and product leaders in a fast-moving AI team where ideas ship quickly and impact is immediate.Contribute to a culture of experimentation, clarity, and high-quality execution.Build prompt architectures, system instructions, and orchestration logic that ensure reliability, grounding, and personality consistency.Prototype new capabilities rapidly and iterate based on user signals and evaluation data.