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. Requires fluent Russian, as part of the team communication is conducted in Russian Originally posted on Himalayas