Senior ML Engineer - MARL & Logistics Optimization

Snoonu; Doha; Hybrid; QAR 180,000 - 300,000; Full time

Benefits offered by this job

Remote & Global Vibes – Collaborate w/

Learning budgets

Builder’s autonomy

Flexible time off

Agile environment

Job summary

Snoonu in Qatar seeks a backend ML engineer to develop and deploy machine learning and optimization models powering a hybrid autonomous logistics platform that coordinates human couriers, robots, and drones.

You will design optimization strategies for task allocation, routing, scheduling, and dispatch, and build MARL models to improve fleet coordination under dynamic demand and traffic. Strong Python/C++ skills and cloud experience are required.

Qualifications

• Bachelor’s or Master’s degree in ML/AI/CS or related field.

Responsibilities

• Develop and deploy ML and optimization algorithms for Snoonu’s logistics platform.
• Design optimization for task allocation, routing, scheduling, and dispatch.
• Develop MARL models to improve fleet coordination under dynamic demands.
• Build demand forecasting and spatiotemporal prediction models.
• Develop energy-aware optimization for battery use, charging, and sequencing.
• Integrate models into the RaaS orchestration platform with low latency.
• Evaluate model performance with metrics like routing efficiency and ETA.
• Implement experiments, offline benchmarking, simulation validation, field pilots.
• Support model lifecycle management and retraining strategies.
• Document architecture, validation results, and experiment methods.

Skills

Backend development

Python

C++

MARL

Graph modeling

Cloud services

Communication

Education

Bachelor’s or Master’s in ML/AI/CS/DS/OR

Tools

FastAPI

Flask

Django

AWS

DynamoDB

S3

Job description

Snoonu in Qatar seeks a backend ML engineer to develop and deploy machine learning and optimization models powering a hybrid autonomous logistics platform that coordinates human couriers, robots, and drones.

You will design optimization strategies for task allocation, routing, scheduling, and dispatch, and build MARL models to improve fleet coordination under dynamic demand and traffic. Strong Python/C++ skills and cloud experience are required.