Machine Learning Engineer

Must Have

5+ years of machine learning engineering experience building and deploying deep learning models in production.

Experience building regression, forecasting, or other supervised machine learning systems for production prediction tasks.

Expert-level proficiency in Python and its core data science libraries, including PySpark, Pandas, NumPy, Scikit-learn, PyTorch, and gradient-boosting libraries such as CatBoost, XGBoost, and LightGBM.

SQL.

Ability to design an ML system from scratch, including data analysis and processing.

Experience translating business goals into ML problems with appropriate metrics and non-functional requirements.

Experience designing and evaluating ML experiments.

Experience with MLOps tools.

Experience working with large-scale geospatial and behavioral datasets.

Experience deploying models to production on ML serving infrastructure and optimizing for latency, with awareness of concept drift and how to detect and manage it.

Comfort working with large-scale geospatial and behavioral data, such as GPS traces and H3 spatial indexing.

Hard Skills / Nice to Have

Academic background in Computer Science, Mathematics, or a related discipline.

Experience with travel time prediction, traffic estimation, or routing quality.

Experience with open-source routing engines.

Knowledge of map matching, speed profiles, road graph tiles, and historical traffic.

Experience with mapping, location, or geospatial products.

Experience building products for developing markets.

Experience with cloud data and machine learning platforms.

Responsibilities and Tasks

Design and build machine learning models to improve routing and travel time prediction.

Develop traffic estimation models using large-scale GPS data.

Implement map-matching solutions for noisy GPS data.

Improve travel time calculation, smoothing, and rerouting logic.

Translate routing objectives into machine learning objectives and evaluation metrics.

Lead offline and online model evaluation activities.

Collaborate with backend engineers to deploy low-latency production models.

Partner with product and operations teams to define new features and requirements.

Own the production ML lifecycle, including serving, monitoring, drift detection, and retraining pipelines.

Technology Stack

Python, SQL, PySpark, Pandas, NumPy, Scikit-learn, PyTorch, XGBoost, LightGBM, CatBoost, MLOps, ML Lifecycle Management, Production ML Systems, ML Infrastructure, Geospatial Analytics, GPS Data, H3 Spatial Indexing