About the role
Ello’s mission is to maximize the potential of every child, everywhere. To close that gap, we’re building the world’s first AI teacher: one that listens, speaks, adapts, and inspires, just like the best human educators. Our first product, Read with Ello, has already helped hundreds of thousands of kids each week learn to read. It listens as they read aloud, offers support when they stumble, and generates magical, personalized stories. It works, and kids love it. Now, we’re scaling that success into something even bigger: a complete AI teacher for all children. Our new app combines language, speech, and memory to deliver interactive learning for kids worldwide – check it out here . We’re moving fast: we ship weekly, test directly with kids, and push the boundaries of what AI can do in education.
About
the role We’re looking for an AI/ML Engineer to join our product engineering and applied research team. Our team operates at the intersection of research and production, solving open-ended problems while delivering real-world products. We use the best tool for the job, whether that’s a novel machine learning model or plain software engineering fundamentals. You’re excited to build an AI product that pushes the boundaries of what’s possible in education. You believe that great products and research strengthen each other. You’ll thrive here if you enjoy shipping AI features into a real product, designing the evaluations that prove they work, and turning fuzzy product questions into measurable experiments. You’ll make an edit in your Jupyter notebook this week, deploy it on-device next week, and watch it bring smiles to kids’ faces a day later. What You’ll Do Help define the impact of AI on children’s education. Own AI systems and features end-to-end: from identifying product opportunities, to building and operating research and evaluation systems, to shipping production improvements used by children worldwide. Collaborate with learning experts and product engineers to build infrastructure measuring the educational quality, engagement, safety, latency, and behavior of an AI teacher Design and improve the agent harness that powers Ello’s tutoring experience, including how the tutor decides what to teach next, when to intervene, and how to adapt to each child Develop learner profiling and adaptation systems that build a model of each child – their strengths, gaps, pace, and engagement patterns – and use that model to drive instructional decisions Develop feedback loops and data flywheels that continuously improve product quality through usage, evaluation, and experimentation About you Experience : 3+ years of experience building AI products in an engineering or research role. You’ve worked in environments that require you to take a high level of ownership. Software engineering fundamentals: You take pride in writing clean Python code that others can build upon. You’re able to get up-to-speed quickly on unfamiliar code, work well with other engineers of varying backgrounds, and debug complex systems. You can make technical decisions with incomplete information while still maintaining high engineering standards. Evaluation and product improvement: You’ve built and operated evaluation or measurement systems, such as AI evals. You can own the loop from ambiguous product quality questions, to concrete metrics and research plans, back to production improvements. Research and experimentation: You’re comfortable with experimental thinking and reasoning through how to build systems around improving a non-deterministic product, while the underlying AI foundations shift. You can think empirically, but also make good calls in the gray area when needing to weigh conflicting signals and move quickly. Machine learning foundations: You’re able to read recent ML papers and implement parts of the stack – you don’t need deep model training expertise, but you do need to understand how they work and how to evaluate them. You have sufficient proficiency in applied mathematics and linear algebra to contribute to research discussions. AI-forward workflow: you have a working practice of leveraging AI in your engineering workflows, and an active curiosity in how AI is transforming work.