At Moxie, we empower ambitious aesthetic entrepreneurs to build profitable, independent practices—without burnout, overwhelm, or guesswork. In just a few years, we've grown from an idea to a global, remote-first team now supporting 700+ practices nationwide.
Our purpose is simple: to unlock sustainable success for aesthetic entrepreneurs, at every stage of their journey.
We're looking for a Staff AI Engineer to lead some of Moxie's highest-impact AI projects in Q4 2026 and H1 2027, including Maia, the Context Engine, and AI drafts. Beyond the project work, you'll provide technical leadership across the company and own and validate major architectural decisions that shape how Moxie builds, with particular responsibility for our AI systems and shared AI platform.
You'll join the Client Experience team and work day to day with Product, Design, QA, Operations, and your engineering peers. You'll inherit live LLM features and a growing shared platform underneath them, including customer-facing systems where quality is visible to patients and providers. The bar is production, not demos.
Become a primary IC on Moxie's AI initiatives. Within your first month you're contributing to production AI systems, understanding our architecture and key workflows, and identifying opportunities for improvement. By month three you're a leading technical contributor on one or more AI initiatives, including Maia, AI drafts, or the Context Engine.
Ship early wins that improve AI quality and reliability. Within your first two months, deliver targeted fixes and improvements to known failure modes, evaluation coverage, or production reliability, with measurable evidence that they worked.
Lead a major AI project from design to production. Take one meaningful AI initiative through design, architecture review, cross-functional collaboration, and launch, including the evaluation and guardrail systems needed to operate it reliably. By month six, lead a second, more ambitious initiative to a visible milestone or production-ready state.
Shape architecture across the company. Within your first month, you're actively participating in architectural discussions and identifying opportunities to improve Moxie's systems. By month three, you're leading technical proposals, influencing major architectural decisions across teams, and taking designs from proposal through review, approval, and implementation, with particular ownership of our AI architecture and shared platform.
Make AI quality measurable and scalable. Within six months, our AI features have measurable quality scorecards, regressions are caught before production, and the Context Engine is becoming the standard data path for AI surfaces, supported by shared evaluations, guardrails, and observability.
Own non-project technical initiatives and engineering culture. Start owning at least one org-level initiative in your first three months (for example AI evaluation standards, agentic SDLC improvements, code review practices, or documentation culture), and by month nine own one or two initiatives with measurable gains in quality or velocity, while shipping v1 of one or more key initiatives.
Staff-level engineering experience. You've led large, ambiguous initiatives as a hands-on IC, owning delivery, tradeoffs, and major architectural decisions across teams.
Production LLM experience. You've led the design and delivery of LLM agents or generative features that real customers depend on, not just prototypes or internal tools.
Technical leadership and engineering standards. You've improved engineering practices, influenced peers across teams, and worked with Product, Design, and engineering leadership to resolve complex decisions and raise the technical bar.
AI evaluation and quality engineering. You've built evaluation systems for production AI, combining automated tests, agent traces, human feedback, and production monitoring to measure quality, catch regressions, and continuously improve reliability.
Sound agent and tool design. You know when to use deterministic code versus an LLM, design safe tool boundaries and human handoffs, and manage reliability, security, latency, and cost tradeoffs. You make pragmatic build-versus-buy and model-provider decisions.
Strong backend and platform engineering. You're comfortable with Python and Django, Postgres, Redis, async workers, and REST or GraphQL, and can design shared AI infrastructure with clear contracts, caching, and tenant isolation.
This is not a role for someone who starts by swapping models or rewriting prompts before measuring what is actually broken.
Ship production AI that patients and medspa staff use every day. Maia answers medspas' texts and calls, and AI drafts go out from the provider's own number, so your work is visible to real people.
Breadth across several AI products and the shared platform underneath them, with real architectural influence as a decision-maker on the architecture review board.
A proprietary data advantage. You'll build on clinical and practice data from roughly 1,200 medspas that competitors can't access.
High-trust, high-ownership IC work at staff level, with a say in the AI roadmap and in how Moxie builds AI.
Above all, we're in this to empower small business owners to own and grow their businesses. If you succeed, Med Spas will remain the opportunity for independent owner-operators instead of being dominated by a small number of large chains. If that speaks to you, speak to us.
Competitive salary
At Moxie, we believe in creating a workplace where everyone feels valued, trusted, and included. Our team lives by our values: act as owners, give more than we take, move with speed and care, and simplify and learn every day.
We welcome people of all backgrounds, experiences, and perspectives to apply. If you require any accommodations to fully participate in the interview process, please let us know, we’re happy to assist.