Machine Learning Engineer | Global Remote
Singapore
Listed on 2026-01-30
-
IT/Tech
Machine Learning/ ML Engineer, Data Scientist, AI Engineer, Data Analyst
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 OverviewWe 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.
- 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, Tensor Flow, or PyTorch.
- Familiarity with Pandas / Num Py.
- 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
- 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.
- 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-cor…
(If this job is in fact in your jurisdiction, then you may be using a Proxy or VPN to access this site, and to progress further, you should change your connectivity to another mobile device or PC).