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 you additionally need to know : This is a full-time, onsite role based in our Bengaluru (MG Road) office. The role follows UK collaboration hours (11 :00 AM8 :00 PM IST) to support seamless cross-regional execution. This role is designed with a growth path to Senior Machine Learning Engineer.

What You'll Be Responsible For :

Design and deploy ML models 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

- Build regression, classification, ranking, and time-series models using structured logistics data, transactional data, and behavioural data.

Own the ML lifecycle :

- 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

Collaborate cross-functionally :

- 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

- 3+ years of experience in Machine Learning, Applied ML, or Data Science roles

- Experience building and deploying ML models into production

- Prior experience building fraud detection models

- Experience in SaaS, e-commerce, fintech, logistics, or marketplaces

- Strong Python (Pandas, NumPy, Scikit-learn, XGBoost/LightGBM/CatBoost)

- Advanced SQL proficiency

- Experience working with structured and large-scale datasets

- Experience building ML pipelines and production APIs

- Familiar with GCP ecosystem (BigQuery, Airflow, Dataform, Vertex AI)

- Strong understanding of regression & classification models, imbalanced datasets, feature engineering for behavioural data, time-series forecasting, ranking/recommender systems, and experiment design & evaluation metrics

- Bachelor's degree in Computer Science, Engineering, or a related technical field

- Strong problem-solving ability in ambiguous environments, business-oriented and impact-driven, comfortable owning systems end-to-end

If you're excited about this role but don't meet every requirement, we'd still love to hear from you.

What We Bring to the Table as an Employer :

- Generous remuneration and stock units

- Comprehensive health coverage

- We reimburse gym and wellness expenses so you can invest in your health

- Zomato digital meal credits and a pantry full of wholesome snacks to keep you fuelled through the workday

- The freedom to 'Work from Anywhere' for 4 weeks in a year

- Generous vacation policy, plus duvet days and mental health days to truly recharge

- 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.

Headquartered in London with offices in New York, Hong Kong, Bangalore, Singapore, Melbourne, Toronto, and Taipei our team is global and growing. We encourage you to apply if a challenge excites you. Come and join the Easyship team!

Don't take it from us, take it from Cristina. One of our longest serving backend engineers.