Remote/Full-time

Competitive salary in USD – $6k - $7к

About Rafeeq

Rafeeq is a rapidly growing on-demand delivery platform connecting customers with restaurants and couriers across the region. We're solving complex marketplace problems at scale - balancing supply and demand in real-time to create exceptional experiences for everyone on our platform.

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

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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

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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

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Strongly Preferred:

— Experience in marketplace companies (food delivery, ride-hailing, e-commerce)

— Domain expertise in dispatch, routing, dynamic pricing, or incentive systems

— Experience with optimization algorithms (linear programming, reinforcement learning, combinatorial optimization)

— Familiarity with streaming data (Kafka, Kinesis) and real-time ML systems

— Experience with MLOps tools and practices (feature stores, model registries, monitoring)

— Knowledge of geospatial libraries (PostGIS, GeoPandas, H3, Kepler.gl)

— Publications or contributions to open-source ML projects

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Nice to Have:

— Experience with Graph Neural Networks (GNNs) for routing/dispatch problems

— Background in operations research or supply chain optimization

— Experience with causal inference for measuring treatment effects

— PhD in a relevant field

Why Join Rafeeq?

— High Impact: Solve critical marketplace problems affecting thousands of couriers and millions of customers daily

— Greenfield Opportunity: Build these systems from scratch with modern tools and best practices

— International Team: Remote-first culture, work from anywhere, competitive salary in USD/EUR—

— Fast-Paced: Ship quickly, iterate based on data, see immediate impact of your work

— Technical Excellence: Work with a strong ML team led by an experienced Team Lead, learn from each other

— Competitive Compensation: Market-rate salary in foreign currency + equity in a growing company

— Ownership: Own entire problem domains end-to-end, from research to production to iteration

Problems We're Solving

Food Delivery Focus: Our primary challenge is optimizing the food delivery marketplace with focus on ETA accuracy, intelligent dispatch, and dynamic pricing based on demand forecasts.

Taxi Vertical (Future): We're also building a taxi service facing incentive optimization challenges - demand prediction, surge pricing, and smart courier positioning.

You'll initially work on food delivery problems, with potential to expand to taxi as we scale the team.

Hiring Process

— Application Review: HR team screens applications - we respond quickly with initial interest

— Team Interview: Technical discussion with ML team members about your experience and approach

— Final Interview: Deep technical conversation with Anton (Team Lead) covering ML expertise, problem-solving, and collaboration

— Offer: Fast decision-making, typically 2-3 weeks from application to offer

We hire internationally and move faster than most companies - strong candidates often receive offers within 2-3 weeks.

What Success Looks Like - First 90 Days

— Deep understanding of our dispatch, pricing, and incentive systems - current state and opportunities

— Ship first model improvement to production (dispatch OR surge OR incentives)

— Design and launch A/B experiment to measure impact

— Build monitoring dashboards for your problem area

— Establish strong working relationships with Product, Engineering, and ML team

— Contribute to technical roadmap for your focus area