Lead AI Engineer

About Euphoric

Euphoric builds AI-first employee benefits administration software for the world’s largest and most prestigious employers. Born out of Peppy Health — a fast-growing, category-defining Series B startup — we spun out in May 2025 to tackle a far bigger opportunity.

Global employers spend $3–5 trillion on benefits every year. It’s their second-largest cost after payroll — yet the entire category is still running on legacy systems and outdated assumptions. The space is primed for transformation: despite its scale, it’s been almost completely overlooked by AI-first startups. Incumbents — including one U.S. heavyweight with $2B+ in annual revenue — are constrained by archaic tech stacks and can’t move fast enough to innovate.

And this isn’t just a massive market — it’s genuine “tech for good.” Smarter benefits administration, richer analytics, and clearer communication have the power to directly improve the everyday lives, wellbeing, and financial security of millions of employees worldwide.

We’re a sharp, high-velocity team with bold ambitions, obsessed with building category-defining technology and partnering with exceptional people who want their work to truly matter.

We closed our seed round in October 2025 and are building from a position of strength with a long cash runway.

We are looking for a Lead AI Engineer to drive the technical evolution of our AI-first platform and serve as our resident expert. You won't just be tuning hyperparameters in a notebook; you will be a core software engineer architecting the complex systems that make AI useful in production.

In the immediate future, your time will be split between crafting complex AI processing architectures (40%) and hands-on data/backend engineering (40%), with the rest spent doing whatever is needed to ship. As we mature, your focus will shift toward closing the loop: using real-user data to optimize the platform via online evaluation and targeted, data-driven models.

• Architect & Engineer AI Systems: You won’t just call an API; you will build robust processing architectures that leverage off-the-shelf LLMs, RAG pipelines, and context engineering to solve hard problems. You will benchmark these systems rigorously to ensure they actually work, not just "feel" smart.

• Build the Backend Foundation: You will spend significant time in Python (FastAPI, SQL Alchemy) building the APIs, data structures, and services that power the product. You are an engineer who ensures the "AI" is delivered on reliable, production-grade rails.

• Close the Data Loop: As we launch, you will lead the transition from "zero-shot" implementations to data-driven optimization. You will instrument data collection, design online evaluation metrics, and deploy simple, effective ML models (e.g., logistic regression, boosted trees) to optimize user outcomes based on real feedback.

• Be a "Full Stack" Partner: While the backend/data is your home, you are willing to jump into the frontend (React/TS) to implement features (AI or not) or debug an integration. You understand that an algorithm isn’t "done" until the user interacts with it successfully.

• Mentor & Lead: You will work alongside full-stack engineers, upskilling the team on AI concepts and best practices. You are the "from-the-trenches" expert who helps the team distinguish between Twitter hype and engineering reality.

• Strategic Advising: You will help Product and Leadership navigate the map of what’s valuable and what’s possible, steering the roadmap toward features that achieve business impact through data-driven optimization.

• Production Engineering (5+ years): You are a strong software engineer first. You have extensive experience building and maintaining backend services in Python (ideally FastAPI or similar async frameworks) deployed to modern cloud providers (GCP/AWS) or point solutions (Railway, Render, Vercel)

• Applied AI Architecture: You have built complex applications using Foundation Models / LLMs. You understand context window management, prompting strategies, and how to chain models together to achieve reliable results.

• True ML Fundamentals: You aren't limited to APIs.…