Pune, Maharashtra, India
Information Technology
Full-Time
Swiggy
Overview
Job Profile: Machine Learning Engineer: I
Location: Bangalore | Karnataka
Years of Experience: 1 - 3
Position Overview
You will be part of Swiggy’s Machine Learning Platform team, building the foundations that power large-scale prediction systems such as ETA, pricing, and demand forecasting.
This role combines applied modeling, data science, and engineering rigor — enabling models to be not just accurate in research but reliable, explainable, and production-ready at scale.
What qualities are we looking for?
Location: Bangalore | Karnataka
Years of Experience: 1 - 3
Position Overview
You will be part of Swiggy’s Machine Learning Platform team, building the foundations that power large-scale prediction systems such as ETA, pricing, and demand forecasting.
This role combines applied modeling, data science, and engineering rigor — enabling models to be not just accurate in research but reliable, explainable, and production-ready at scale.
What qualities are we looking for?
- Strong grasp of machine learning fundamentals — supervised learning, regularization, validation strategies, and performance metrics.
- Working understanding of statistics and data analysis, including variance, bias, correlation, and uncertainty.
- Proficiency in Python and numerical computing; ability to reason about algorithms and data flow.
- Clear, reproducible coding style and comfort handling large-scale structured data.
- Collaborative and analytical mindset with curiosity to understand why models behave the way they do.
- Exposure to distributed data systems or ML pipelines (e.g., batch and real-time).
- 1–4 years of experience in applied ML, data science, or software engineering (scope calibrated to level).
- Enthusiasm for building practical, explainable, and scalable ML systems that make measurable impact.
- Prior experience developing or deploying ML models in production environments.
- Contribute to the design, training, and evaluation of ML models with a focus on reproducibility, stability, and generalization.
- Build and maintain scalable pipelines for model training, validation, and deployment.
- Apply sound statistical reasoning to assess data quality, feature behavior, and model diagnostics.
- Collaborate with scientists and engineers to translate analytical insights into robust ML components.
- Support the continuous monitoring and improvement of model performance over time.
- Champion good ML practices — controlled experiments, documentation, and interpretability.
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