Senior Machine Learning Engineer

Join Kiddom's Data Science team as a Senior Machine Learning Engineer. You will architect and scale machine learning systems for search, personalization, and recommendations that directly support teachers and students. Your work will involve developing evaluation-first workflows, fine-tuning models, designing intelligent discovery pipelines, and building agentic assistants. You will collaborate closely with product managers, designers, and curriculum experts, and coach and mentor junior ML engineers and data scientists. Enjoy meaningful equity, a remote-friendly culture, health benefits, and a flexible vacation policy.

• 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.
• Collaborate closely with product managers, designers, and curriculum experts to translate high-level educational goals into scalable ML-powered systems.
• 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 experiment
• Excellent communication skills with a history of cross-functional collaboration with product, design, and engineering stakeholders
• 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