AI Product Engineer

About Us

Mathspace is on a mission to transform mathematics education. We believe that every student can learn and grow in mathematics with the right support—and we’re building the most intelligent, adaptive platform to make that possible.

For over a decade, we’ve pioneered interactive, feedback-driven math learning experiences used by hundreds of thousands of students and teachers globally. But we’re just getting started.

The future of math education is one where AI helps every learner receive personalized, real-time guidance—just like a tutor would. And we’re building it.

We’re a passionate, mission-driven team that blends deep educational insight with cutting-edge technology. At Mathspace, your work directly impacts how the next generation learns.

About the Role

As an AI Product Engineer at Mathspace, you'll lead the development of intelligent learning experiences powered by large language models (LLMs) and other forms of generative AI. From concept to production, you’ll build novel product features that help students get timely hints, conversational feedback, and human-like math support—at scale.

You’ll join a cross-functional product squad of engineers, designers, educators, and product managers, and work across the stack to explore what’s possible at the intersection of pedagogy and AI. You'll evaluate models, design prompts and evaluation frameworks, and help us ship polished experiences to classrooms around the world.

We're still in the early days of our AI journey, and you'll be a key contributor in shaping our strategy, systems, and culture around building with LLMs.

What You’ll Be Doing

• Designing and building AI-powered learning experiences that integrate LLMs into Mathspace’s student and teacher-facing apps.
• Prototyping, testing, and deploying LLM-based features, such as math-specific feedback, adaptive hints, automated question generation, and teacher co-pilot tools.
• Working across the stack using React, TypeScript, Python and GraphQL.
• Experimenting with prompting, RAG, vector search, and fine-tuning strategies to optimize for student learning outcomes.
• Collaborating with our curriculum and pedagogy teams to ensure our AI experiences are pedagogically sound and aligned with our educational values.
• Contributing to our AI infra and LLMOps processes, including evaluation pipelines, prompt versioning, and observability tools.
• Thinking creatively and critically about what LLMs should and shouldn't do in an educational context—and designing safeguards accordingly.