Machine Learning Engineer

• 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 AM–8:00 PM IST) to support seamless cross-regional execution. This role is designed with a growth path to Senior Machine Learning Engineer. • What you additionally need to know: • What would be you be responsible for: • 1. 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 • 2. Build regression, classification, ranking, and time-series models using: • Structured logistics data • Transactional data • Behavioural data • 3. 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. • 4. 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… • You have 3+ years of experience in Machine Learning, Applied ML, or Data Science roles. • You have experience building and deploying ML models into production. • You have prior experience building fraud detection models • You have experience in SaaS, e-commerce, fintech, logistics, or marketplaces • You have strong Python (Pandas, NumPy, Scikit-learn, XGBoost/LightGBM/CatBoost). • You have advanced SQL proficiency. • You have experience working with structured and large-scale datasets. • You have experience building ML pipelines and production APIs. • You are familiar with GCP ecosystem (BigQuery, Airflow, Dataform, Vertex AI). • Regression & classification models • Imbalanced datasets (important for fraud) • Feature engineering for behavioural data • Time-series forecasting • Ranking/recommender systems • Experiment design & evaluation metrics • You have a Bachelor’s degree in Computer Science, Engineering, or a related technical field • You have strong problem-solving ability in ambiguous environments. • You have business-oriented and impact-driven • You are hands-on and execution-focused • You are comfortable owning systems end-to-end • You are clear communicator across technical and non-technical stakeholders • You are motivated to grow into a Senior ML Engineer role • 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 pantry full of wholesome snacks to keep you fueled 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: