Mathspace3 followers6 jobs

AI Product Engineer

Mid levelSeniorFull-timeRemoteSydney NSW, AustraliaAustraliaProductSoftware engineeringPosted 1 month agoVerified 1 month ago
Pending fitX of Y criteria met

About the job

The AI Product Engineer job at Mathspace is an exciting opportunity to lead the development of intelligent learning experiences powered by large language models (LLMs) and generative AI. This position plays a crucial role in transforming mathematics education by creating features that provide students with timely hints and conversational feedback, enhancing their learning journey. Mathspace fosters a collaborative environment where a passionate team works together to innovate and impact how the next generation learns.

Meet the team

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Think you're a good fit?See what the hiring team are looking for

You'll be responsible for

🛠️

Designing and building AI-powered learning experiences

Integrating LLMs into Mathspace’s student and teacher-facing apps to enhance learning.
🚀

Prototyping, testing, and deploying LLM-based features

Creating features like math-specific feedback, adaptive hints, and automated question generation.
🤝

Collaborating with curriculum and pedagogy teams

Ensuring AI experiences are pedagogically sound and aligned with educational values.

Key criteria

🖥️

5+ years of full stack or backend engineering experience

Proven experience in product-focused engineering roles.

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⚙️

Proficiency in React, TypeScript, Python, and GraphQL

Hands-on skills in the required technology stack.

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🤖

Experience with LLM-powered applications

Demonstrated ability to build and deploy AI-driven solutions.

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A meaningful career starts with a match

View your fit

5 criteria for this job
5+ years of full stack or backend engineering experience
Proven experience in product-focused engineering roles.

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Proficiency in React, TypeScript, Python, and GraphQL
Hands-on skills in the required technology stack.

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Experience with LLM-powered applications
Demonstrated ability to build and deploy AI-driven solutions.

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Familiarity with RAG and prompt engineering
Knowledge of evaluation frameworks and LLMOps best practices.

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Strong communication and collaboration skills
Ability to work effectively across diverse teams.

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