Hands-On ML Engineering (60% of time)

• Own ETA Prediction Systems: Design, build, and deploy ML models for restaurant preparation time, courier travel time, and dynamic delivery estimates
• Solve the Food Delivery Challenge: Tackle our critical ETA accuracy problem which impacts customer satisfaction and operational efficiency
• Feature Engineering: Build robust pipelines for temporal, geographic, and contextual data
• Model Development: Research and implement state-of-the-art techniques including time-series models, deep learning (LSTMs, Transformers), and ensemble methods
• Production Deployment: Take models from research to production in real-time, low-latency environments
• A/B Testing & Iteration: Design experiments to measure model impact and continuously improve accuracy

Cross-Functional Collaboration

• Work closely with Product team to understand business requirements and priorities
• Partner with Engineering to integrate ML models into production systems
• Collaborate with Product Analyst to define metrics and measure success
• Present results and insights to stakeholders across the organization

What We're Looking For

Required Qualifications:

• 5+ years of hands-on ML/Data Science experience with at least 2+ years in a technical leadership role (team lead, tech lead, or senior IC with mentorship responsibilities)
• Proven expertise in time-series forecasting and prediction models (ARIMA, Prophet, LSTM, Transformers, or similar)
• Production ML experience: Track record of deploying models in high-throughput, low-latency environments
• Strong programming skills in Python; expert-level knowledge of ML frameworks (TensorFlow, PyTorch, Scikit-learn)
• Deep SQL proficiency for feature engineering and data analysis
• Leadership experience: Comfortable mentoring engineers, conducting interviews, and making technical decisions
• Communication skills: Ability to explain complex technical concepts to both technical and non-technical audiences
• Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or related field

Strongly Preferred:

• Experience in logistics, food delivery, or ride-hailing companies (Yandex, Delivery Club, Ozon, WB, Magnit etc.)
• Domain expertise in ETA prediction, routing, or travel time estimation
• Experience with geospatial data and libraries (PostGIS, GeoPandas, H3)
• Familiarity with MLOps, model monitoring, and experimentation frameworks
• Experience with real-time streaming technologies (Kafka, Kinesis)
• Track record of mentoring and growing junior engineers

Why Join Rafeeq?

• Impact: Your models will directly affect millions of deliveries and be the foundation of our platform
• Leadership Opportunity: Build and lead the ML function in a high-growth startup
• International & Remote: Work from anywhere, collaborate with global talent, get paid in USD/EUR
• Fast Hiring: We move quickly (2-3 months typical, often faster for strong candidates)
• Competitive Compensation: Market-rate salary in foreign currency + equity
• Ownership: Shape technical direction and own critical business problems
• Growth: Opportunity to scale the team and your role as we grow

Hiring Process

1. Initial screening: HR team reviews applications
2. Team conversation: Chat with the ML team to discuss technical approach and experience
3. Final interview: Technical deep-dive covering both leadership and hands-on ML skills
4. Offer: Typically 2-3 weeks from first contact to offer

Success Metrics - First 90 Days

• Establish baseline ETA accuracy and identify top 3-5 improvement opportunities
• Ship first iteration of improved ETA model to production
• Build monitoring dashboards for model performance
• Hire or begin hiring process for 1-2 additional ML engineers
• Establish weekly team rhythm and technical roadmap for Q2-Q3