Machine Learning Engineer
BangaloreFull-TimeMid-levelAI / Data Science
About Easyship
- Easyship is one of the world’s leading multi-carrier shipping software, built to make global eCommerce borderless. Since 2014, we’ve been on a mission to democratize logistics by removing the "black box" of international shipping costs and complexities. Trusted by over 100,000 brands, our platform provides a single "mission control" for global trade, offering access to 550+ courier services across 200+ destinations.
- We are an award-winning, global team (Forbes 30 Under 30, TechInAsia’s Best Startup) with offices in London, New York, Hong Kong, and beyond. We’re growing fast, we value transparency, and we genuinely enjoy building the infrastructure that powers modern commerce. If you're ready to solve complex problems at scale, we’d love to have you join us.
Who We’re Looking For
- We are seeking a Machine Learning Engineer at Easyship, who will build and scale our predictive intelligence systems across pricing, logistics automation, fraud detection, and revenue optimization. You will work on high-impact ML systems such as pricing optimization, delivery promise prediction, service recommendation, fraud detection, propensity modeling, HS code classification, and auto-filling shipment details. Your models will directly influence customer experience, operational efficiency, and platform trust.
- This is a hands-on role in a lean and fast-moving environment. You will partner closely with Product, Engineering, Business, and Operations teams to turn ambiguous business problems into production-ready ML systems. You will own the ML lifecycle end-to-end - from data exploration and feature engineering to deployment, monitoring, and iteration.
What would be you be responsible for
- Fraud detection models (high priority focus)
- Pricing optimization
- Propensity modeling (upsell, conversion & lead scoring combined)
- Product Classification
- Auto-filling shipment details from structured and semi-structured data
- and others!
2. Build regression, classification, ranking, and time-series models using
- Structured logistics data
- Transactional data
- Behavioural data
- Frame business problems into well-defined ML problems.
- Perform exploratory data analysis and feature engineering.
- Train, evaluate, and validate models.
- Deploy models into production with engineering support.
- Monitor performance, detect drift, and iterate.
- Run experiments and measure real-world impact.
- Work closely with Product to clarify ambiguous requirements.
- Partner with Data Engineering to ensure reliable data pipelines.
- Communicate trade-offs, assumptions, and model performance clearly.
- Contribute to defining ML best practices and technical standards.
You Might Be a Good Fit If…
- Regression & classification models
- Imbalanced datasets (important for fraud)
- Feature engineering for behavioural data
- Time-series forecasting
- Ranking/recommender systems
- Experiment design & evaluation metrics
How we value inclusion in our recruitment practices
- Easyship is an equal opportunity employer. We make all employment decisions—recruiting, hiring, pay, benefits, training, promotion, leave, and separation—based on qualifications, merit, and business needs. We do not discriminate on the basis of race, color, religion, sex, sexual orientation, gender identity, marital status, age, disability, national or ethnic origin, veteran or military status, citizenship, or any other characteristic protected by law
