Applied AI Engineer

We’re looking for an Applied AI Engineer to help bring cutting-edge AI capabilities into the hands of developers. This is a hands-on engineering role at the intersection of product, AI, and systems — focused on implementing and integrating LLM-powered features that enhance developer experience and productivity. You’ll collaborate with engineers, researchers, and designers to translate product needs into intuitive, reliable AI-powered experiences. You’ll also play a key role in shaping how AI is used across our stack — from prompt design to system integration — while staying up to date on emerging AI capabilities. Examples of what you could do: Implement and integrate AI functionality into key product features Craft and iterate on prompts to improve LLM reliability and usefulness Build AI-powered flows that feel intuitive and responsive to developers Evaluate and test AI outputs to ensure performance and accuracy Work alongside engineers to deliver robust, production-grade code Stay current with LLM tools, APIs, and best practices You will… Deliver reliable, high-quality AI-powered product experiences Translate product needs into technical AI implementations Tune and test prompts for real-world use cases and developer workflows Collaborate closely with engineers and researchers Contribute across frontend, backend, and integration layers Qualifications: Strong coding skills in one or more of: Python, Go, Node.js, JavaScript, TypeScript, React, or Java Experience with API integrations and service-oriented architectures Familiarity with prompt engineering for LLMs (e.g. OpenAI, Claude, Gemini) Ability to evaluate and optimize AI outputs for reliability and quality Strong problem-solving instincts and attention to detail Collaborative mindset and eagerness to learn Bonus Points: Experience building product features that incorporate LLMs Understanding of best practices for AI reliability and safety Background in frontend development or UX-oriented implementation Familiarity with cloud platforms (especially GCP) Basic understanding of LLM behavior, strengths, and limitations