About Brilliant Brilliant is creating a world of better problem solvers. We deliver learning that's interactive, adaptive, and fun – at scale. You can learn more about our approach, learners, and method on our about page. We have always prioritized building a real, healthy business, and we make hires very selectively and intentionally. We serve hundreds of thousands of paid subscribers, and want you to help us serve millions. In our day-to-day, we value adventure, excellence, generosity, and candor. We are optimists in the face of uncertainty, we take pride in our work, we go the extra mile for each other, and we tell it like it is (the good and the bad). We’re here to do the best work of our lives together. We believe that real-time collaboration and human connection are necessary ingredients in building a high-velocity, high-trust team. We maintain core hours (9:30am - 2:30pm Pacific) when everyone is online, regardless of timezone. About half of us are located near our hubs in SF and NYC. In addition to what’s below, you can see all open roles and learn more about our culture on our careers page. The Role Engineers at Brilliant work in small, elite teams alongside colleagues from Product, Design, and Content. As an interactives engineer, you’ll craft the building blocks that power Brilliant's interactive learning experiences. Brilliant's learning experiences blend thoughtful mechanics, intuitive level design, and deep pedagogical insights. Each interactive is driven by APIs designed for learning designers, our LLM-powered content agent, and our AI tutor, with every possible configuration guaranteed to be a correct, solvable, and meaningful puzzle. It's a tricky technical problem with a big payoff for our learners. Solving this technical challenge enables us to build effective and engaging learning experiences at scale, helping our learners get the reps they need to achieve mastery. With your help, we'll teach all of STEM. Check out these posts to learn more:
- A world-class tutor in every home
- Hand-crafted, machine-made: How we make learning games with AI
- When “almost right” is catastrophically wrong: Evals for AI learning games