Senior Routing ML Engineer
Не указаноПольша, Казахстан, ЕС, Грузия, Армения, Сербия
SeniorRemoteE-commerceКачество текста 2/5EN B2+
Machine LearningPythonSQL12д
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.
Hard Skills / 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 (e.g., PySpark, Pandas, NumPy, Scikit-learn, PyTorch; gradient-boosting libraries such as CatBoost/XGBoost/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, and awareness of concept drift and how to detect and manage it.
• Comfort working with large-scale geospatial and behavioral data (e.g., GPS traces, H3 spatial indexing).
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.
• 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.
Hard Skills / 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 (e.g., PySpark, Pandas, NumPy, Scikit-learn, PyTorch; gradient-boosting libraries such as CatBoost/XGBoost/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, and awareness of concept drift and how to detect and manage it.
• Comfort working with large-scale geospatial and behavioral data (e.g., GPS traces, H3 spatial indexing).
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.