
Innovation Engineer
4 weeks ago
We are seeking an Innovation Engineer with a strong background in algorithms, computational science, and mathematics to spearhead the development of advanced software solutions for math and science education. The ideal candidate will focus primarily on designing robust, algorithmic solutions and computational frameworks, with strategic use of AI tools where they demonstrate clear strengths.
Key Responsibilities- Develop, optimize, and maintain algorithmic solutions for solving and generating mathematical and scientific problems.
- Design software that programmatically creates diverse and challenging math/science question sets, supporting various curricula.
- Collaborate with educators and subject matter experts to translate learning objectives into computational logic and software features.
- Integrate numeric, symbolic, and computational methodologies to produce efficient and transparent educational tools.
- Evaluate and implement AI/LLM-based modules where they complement algorithmic approaches, especially for tasks such as natural-language parsing or automated explanation—while critically assessing their limitations.
- Ensure accuracy, performance, and usability of developed systems.
- Bachelor's or Master's degree in Mathematics, Computational Science, Computer Science, Engineering, or a related quantitative field.
- At least 3 years of hands-on experience in computational roles focused on algorithm development, symbolic/numeric computation, or mathematical modeling.
- Deep conceptual and practical knowledge of mathematical algorithms, data structures, and problem-solving frameworks.
- Strong programming skills in Python, C++, Java, or similar languages, and experience with computation-focused libraries such as NumPy, SymPy, SageMath, MATLAB, or Mathematica.
- Demonstrated ability to build software to analyze, solve, or generate mathematical content programmatically.
- Experience developing educational software involving symbolic/numerical computation or complex algorithmic logic.
- Understanding of the strengths and weaknesses of modern AI tools (including LLMs) as they relate to math and science problem solving.
- Capability to judiciously integrate LLMs or AI-based modules for specific tasks (e.g., parsing word problems) when algorithmic solutions are impractical.
- Analytical and logical reasoning skills for mathematical modeling and software design.
- Passion for advancing math and science education through technology.
- Ability to communicate effectively and work cross-functionally with educators and developers.
- Awareness of recent advances in computational math and select AI approaches, with the discernment to use them where they add distinct value.
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