Machine Learning Research Engineer

What the job involves

• Architect and scale machine learning systems for search, personalization, and recommendations that power Kiddom’s teacher helper and insight engine.
• Develop evaluation-first development workflows to measure how models improve teaching efficiency, lesson planning, and student learning outcomes.
• Fine-tune machine learning models with feedback signals from teachers and students to align outputs with instructional goals and classroom needs.
• Design intelligent discovery pipelines that combine semantic retrieval, curriculum alignment, and real-time personalization.
• Build agentic assistants that help teachers plan lessons, adapt instruction, and reduce repetitive tasks.
• Collaborate closely with product managers, designers, and curriculum experts to translate high-level educational goals into scalable ML-powered systems.
• Coach and mentor junior ML engineers and data scientists, fostering technical and professional growth.

Who you are

• Have 5+ years of industry experience applying machine learning to solve real-world problems with large, complex datasets, with 1–2 years in a technical leadership role.
• Proven track record designing, evaluating, and deploying ML/AI systems in production environments that drive measurable business impact, ideally in recommendation, personalization, search, or workflow optimization.
• Strong programming skills in Python and fluency in data manipulation (SQL, Pandas) and common ML toolkits (scikit-learn, XGBoost, TensorFlow/PyTorch).
• Strong analytical skills and ability to break down complex problems into measurable hypotheses and experiments.
• Excellent communication skills with a history of cross-functional collaboration with product, design, and engineering stakeholders.

Desirable

• Deep expertise in modern deep learning frameworks and advanced LLM architectures.
• Experience building evaluation pipelines for ML/AI systems, ensuring reliable measurement of impact and quality in real-world use.
• Experience implementing and fine-tuning large language models (LLMs), including prompt engineering, embeddings, and efficient inference optimization.
• Familiarity with foundation model adaptation techniques such as PEFT, LoRA, or RLHF.
• 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.