Senior Routing ML Engineer

Customer Description: The customer is a global mobility and urban services platform providing transportation and other on-demand services through a digital marketplace. Project Description: The work spans deep learning model development, large-scale geospatial data pipelines, and low-latency production serving. The candidate will measure the impact through offline evaluation metrics and the results of online experiments Project Phase: ongoing Soft Skills: - Ability to influence teammates and cross-functional stakeholders effectively. - Curious and improvement-oriented mindset with a willingness to challenge existing approaches. - Excellent ability to communicate complex technical findings in a clear and concise manner. 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) Hard Skills / Nice to Have (Optional): - 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 📝 Additional notes: The work will be carried out either on client's equipment or via a virtual workstation.