Machine Learning Engineer - Dispatch, Surge & Incentives
#удаленка #lead #senior #700k
Company: Rafeeq
Salary: от 6 000 до 7 000$
🔹The Role
You will focus on three interconnected problems that are critical to our business:
— Dispatch Optimization - Intelligent courier assignment, order batching, and routing
— Surge Pricing - Dynamic pricing to balance supply and demand in real-time
— Incentive Systems - Smart bonus zones and payments to position couriers where they're needed
These systems directly impact courier earnings, customer wait times, and our marketplace efficiency. Your models will be making thousands of decisions per minute in production.

🔹What You'll Do
Dispatch Optimization
— Build ML models for optimal courier-to-order assignment considering distance, courier state, acceptance probability, and order characteristics
— Implement order batching algorithms to allow couriers to deliver multiple orders efficiently
— Research and prototype advanced techniques: Graph Neural Networks, combinatorial optimization
— Optimize for multiple objectives: delivery time, courier earnings, customer satisfaction, platform efficiency

Dynamic Pricing (Surge)
— Design and deploy surge pricing models that respond to real-time supply-demand imbalances
— Build demand forecasting models at geographic zone and hourly/sub-hourly granularity
— Incorporate external factors: weather, events, seasonality, holidays
— Run A/B experiments to optimize pricing strategies for both customer experience and marketplace balance

Incentive Systems
— Develop models to predict where courier supply will be needed 30-60 minutes in advance
— Build intelligent bonus zone systems to proactively position couriers
— Design incentive structures that maximize courier earnings while improving platform efficiency
— Create attribution models to measure incentive effectiveness

🔹Core Responsibilities
— Model Development: Research, prototype, and deploy ML models for dispatch, pricing, and incentives
— Feature Engineering: Build real-time feature pipelines using geospatial, temporal, and marketplace data
— Production Systems: Deploy models in high-throughput, low-latency environments (p99 < 100ms for dispatch)
— Experimentation: Design and analyze A/B tests to measure impact on key metrics (ETA, courier earnings, order volume)
— Collaboration: Work closely with Product, Engineering, and the ML team to ship features end-to-end
— Monitoring: Build dashboards and alerts to track model performance and marketplace health

🔹What We're Looking For
Required:
— 5+ years of experience in ML/Data Science with at least 3+ years deploying models to production
— Strong ML fundamentals: Regression, classification, time-series forecasting, optimization
— Expert Python skills and deep experience with ML libraries (Scikit-learn, XGBoost)
— Advanced SQL for complex feature engineering and data analysis
— Production ML experience: Real-time inference, model serving, monitoring, A/B testing
— Geospatial data experience: Working with lat/lon, distance calculations, zone-based aggregations
— Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, Operations Research, or related field
Contact: @rafeeq_hr

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