Senior Machine Learning Engineer

About the role

You will architect and scale machine learning systems that drive search, personalization, and recommendations across the Kiddom platform, directly enabling teacher helpers and insight engines that streamline educator workflows. You will own the design of evaluation-first development workflows that measure how models improve lesson planning efficiency, instructional quality, and measurable student learning outcomes in real classroom contexts. In this role, you will fine-tune machine learning models using feedback signals from teachers and students to ensure outputs align tightly with instructional goals and the realities of diverse classroom environments. You will design intelligent discovery pipelines that unify semantic retrieval, curriculum alignment, and real-time personalization to deliver the right resources at the right moment. You will build agentic assistants that support teachers in planning lessons, adapting instruction dynamically, and reducing repetitive administrative tasks so they can focus on student relationships. You will collaborate closely with product managers, designers, and curriculum experts to translate high-level educational objectives into scalable, production-ready ML-powered systems. You will also coach and mentor junior ML engineers and data scientists, fostering both technical excellence and professional growth within the AI team.

Key facts

What you'll do

• Architect and scale production-grade machine learning systems focused on search, personalization, and recommendations that power core educator tools.
• Develop evaluation-first development workflows to quantify how models enhance teaching efficiency, lesson planning effectiveness, and student learning outcomes.
• Fine-tune machine learning models with feedback loops from teachers and students to align model behavior with instructional priorities and classroom constraints.
• Design intelligent discovery pipelines that integrate semantic retrieval, curriculum standards alignment, and real-time personalization for dynamic content delivery.
• Build agentic assistants that automate lesson planning, adapt instruction on the fly, and reduce repetitive tasks for educators.
• Partner with product managers, designers, and curriculum specialists to convert high-level educational goals into technically feasible and impactful ML solutions.
• Implement robust experiment frameworks and evaluation metrics to continuously assess model performance in realistic educational settings.
• Mentor junior machine learning engineers and data scientists, elevating team capabilities in model development, deployment, and monitoring.
• Implement and fine-tune large language models, including prompt engineering, embedding strategies, and efficient inference optimization for latency-sensitive applications.
• Apply foundation model adaptation techniques such as PEFT, LoRA, and RLHF to tailor pre-trained models to the specific needs of K-12 education.
• Maintain strong analytical thinking to break down complex educational problems into measurable hypotheses and structured experiments.
• Ensure that all solutions are production-ready, scalable, and maintain high standards of reliability, security, and performance.
• Communicate technical concepts clearly to both technical and non-technical stakeholders, facilitating alignment across cross-functional teams.
• Stay current with emerging AI research and explore innovative techniques that can be responsibly applied to improve teaching and learning at scale.

Requirements

• Hold 5+ years of industry experience applying machine learning to solve real-world problems involving large, complex datasets.
• Bring 1-2 years of technical leadership experience guiding ML model development, deployment, or data science initiatives.
• Demonstrate a proven track record of designing, evaluating, and deploying ML/AI systems in production that deliver measurable business and educational impact.
• Exhibit strong programming skills in Python and fluency in data manipulation using SQL and Pandas, along with proficiency in common ML toolkits such as scikit-learn, XGBoost, TensorFlow, and PyTorch.
• Show strong analytical skills and the ability to decompose complex problems into measurable hypotheses and controlled experiments.
• Maintain excellent communication skills and a history of effective cross-functional collaboration with product, design, engineering, and curriculum teams.
• Possess deep expertise in modern deep learning frameworks and advanced large language model architectures used in production environments.
• Have hands-on experience building evaluation pipelines that reliably measure model quality, reliability, and impact in real-world educational use cases.
• Demonstrate experience implementing and fine-tuning large language models, including prompt engineering, embedding techniques, and optimized inference strategies.
• Show familiarity with foundation model adaptation methods such as PEFT, LoRA, and RLHF to efficiently tailor pre-trained models.

Nice to have

• Self-motivated innovator who thrives in fast-moving environments and is excited to explore emerging AI techniques to solve meaningful problems in education.
• Passion for applying cutting-edge AI research to improve teaching workflows and personalize student learning at scale.

Practical notes

Full time permanent employees are eligible for benefits from their first day of employment.