Machine Learning Engineer | Global Remote

About the Company EDGE Tutor is Asia’s fastest-growing tutoring outsourcing provider, bringing the human edge in digital learning to education companies worldwide. Founded in 2022 by Harvard alumnus Henry Motte de la Motte, EDGE Tutor now serve partners in over 30 countries with global headquarters in New York, London, Colombo, Singapore, and Manila. We connect the top 3% of rigorously selected online educators with global institutions, delivering white-labeled tutoring solutions with full tutor management. Our services cover K-12 English and Math, Adult/Corporate English, and high-stakes exam prep. At EDGE Tutor, we combine passionate educators with operational efficiency to help education businesses scale and thrive globally. Role Overview We are looking for a senior, hands-on Machine Learning Engineer with strong experience in data warehousing, data pipelines, and applied machine learning. This role focuses on building production ML and AI systems on top of large, real-world datasets generated across 40+ countries and over 1 million lessons per year. You will work on complex operational, quality, and decision-making problems that directly impact how Edge Tutor hires, supports, and scales its teaching workforce globally. Beyond building models, this role will also help drive practical AI adoption across Edge Tutor, embedding ML and AI into day-to-day workflows for operations, recruiting, QA, and client-facing insights. Key Responsibilities Operational Machine Learning Design and deploy ML systems to support teacher reliability, performance, and supply decisions. Build scoring and prediction models used in hiring, assignment, renewal, and removal decisions. Analyse funnel data to identify where strong talent is gained or lost. Compliance, QA & Risk Intelligence Develop ML-driven alerts for operational risk (lateness, absences, complaints, policy violations). Apply anomaly detection and ML on structured data and transcripts. Generate AI-assisted QA summaries and risk reports for internal teams. Positive Signal Detection Identify and surface high-performing tutors early, not just risks. Use ML signals such as learner retention, feedback quality, conversion, and reliability. Feed positive signals into tutor scoring and utilisation logic. Applied AI & Internal Copilots Support AI-driven internal tools such as recruiter, account management, and training copilots. Work with LLM-based systems (where appropriate) for summarisation, recommendations, and decision support. Help teams reduce manual effort and single-person dependencies using AI. Data & Engineering Work with existing data warehouses and pipelines to prepare high-quality ML-ready data. Write clean, production-grade Python code for ML and data workflows. Deploy, monitor, and iterate on ML models in real production environments. Collaborate closely with Product, Ops, QA, and Engineering teams. Required Skills & Experience Must-Have 5+ years experience in machine learning, data engineering, or applied analytics. Strong experience with data warehousing concepts and structured data. Hands-on experience building and running ML models on production data. Advanced Python skills for data processing and ML development. Solid understanding of ML techniques (classification, regression, anomaly detection, forecasting). Comfortable working with imperfect, real-world operational data. AI & ML Stack Experience with ML frameworks such as scikit-learn, TensorFlow, or PyTorch. Familiarity with Pandas / NumPy. Understanding of the full ML lifecycle: training, deployment, monitoring, iteration. Nice-to-Have (Not Required) Cloud experience (AWS / GCP / Azure). Exposure to MLOps practices. Prior EdTech experience What Success Looks Like ML systems actively used in operational and people decisions. Earlier detection of risk and earlier identification of high-potential tutors. Measurable reduction in manual decision-making through ML and AI. Strong adoption of AI-driven tools across internal teams. Work Set-up and Requirements Schedule: Schedule: May require flexible hours but with focus on mid-shift (start of shift at 12NN or 1PM) Workdays: Five-day workweek with two rest days. Equipment requirements: Processor: Quad-core Intel Core i5 (10th Gen+), AMD Ryzen 5 (4000 series+), or Apple M1/M2 chip. Memory (RAM): Minimum 16GB RAM. Storage: 512GB SSD. Display: Full HD (1920x1080) resolution or higher with good color accuracy. Operating System: Windows 10/11 (64-bit) or macOS 11 (Big Sur) or newer. Location: Fully remote Minimum 25 Mbps internet speed with backup connection Backup power supply for at least 8 hours Why Join Us Work on large-scale, real-world ML problems, not toy datasets. High ownership without people-management overhead. Direct impact on product, operations, and client trust. Opportunity to shape how AI is embedded across Edge Tutor. Why Work With Us? Support Educators Globally: Play a key role in supporting our network of expert tutors as they teach learners in 30+ countries across the Americas, Europe, the Middle East and Asia-Pacific Remote-First Culture: Work from home while being part of a collaborative, inclusive team. We meet in person quarterly to strengthen team connections and drive innovation. Structured Flexibility: Flexible but structured hours that help balance your work and life while maintaining work efficiency. Professional Growth: Gain advanced skills in strategic hiring, operational scaling, and decision-making to one of Southeast Asia's fastest-growing startups, recognized with prestigious industry accolades Appealing performance-oriented compensation 30 days paid time off