Senior ML Engineer – Multi-Agent Logistics & Optimization

Snoonu; Qatar; À distance; QAR 279 000 - 502 000; Plein temps

Avantages offerts par ce poste

Remote & Global Vibes

Flexible Time Off

Beyond the Code: Giving Back Matters

Résumé du poste

Snoonu in Qatar seeks a Backend/ML Engineer to develop and deploy ML optimization for its hybrid autonomous logistics platform. You will design task allocation, routing, and dispatch strategies, and build MARL models to coordinate a multi-agent fleet under dynamic demand.

You will forecast demand and predict spatiotemporal patterns to guide proactive planning. The role emphasizes energy-aware optimization, integration with the RaaS platform, and rigorous experimentation, validation, and

Qualifications

• Bachelor’s or Master’s degree in Machine Learning, Artificial Intelligence, Computer Science, Data Science, Operations Research, or related field (PhD a plus).
• 2–4 years of backend development experience, with strong Python/C++ expertise.
• Strong analytical and problem-solving skills for robotics challenges.
• Research-oriented mindset; translate experiments into production improvements.
• Ability to work in cross-functional teams (robotics, embedded, platform/software, operations).
• Clear communication to document work, trade-offs, and validation outcomes.
• High ownership for results, timelines, and engineering quality.
• Adaptability to fast-paced R&D environments with prototyping and testing.
• Strong business context understanding to translate needs into solutions.
• Open to feedback and proactive improvements from seniors.

Responsabilités

• Develop and deploy ML optimization algorithms powering Snoonu’s autonomous logistics platform.
• Design optimization strategies for task allocation, routing, scheduling, and dispatch.
• Develop MARL models to improve fleet coordination under dynamic conditions.
• Build demand forecasting and spatiotemporal models to support proactive planning.
• Create energy-aware optimization for battery usage, charging, and sequencing.
• Integrate ML models into the RaaS orchestration platform with backend engineers.
• Evaluate model performance using defined metrics and support pilot readiness.
• Implement experimentation pipelines for offline benchmarking and field validation.
• Support continuous learning and model lifecycle management.

Connaissances

Python

C++

Backend development

MARL

Reinforcement Learning

Graph Neural Networks

Time-series forecasting

AWS

RESTful APIs

Data analysis

Communication

Cross-functional teamwork

Formation

Bachelor’s or Master’s in ML/AI/CS

PhD in related field (a plus)

Outils

FastAPI

Flask

Django

Description du poste

Snoonu in Qatar seeks a Backend/ML Engineer to develop and deploy ML optimization for its hybrid autonomous logistics platform. You will design task allocation, routing, and dispatch strategies, and build MARL models to coordinate a multi-agent fleet under dynamic demand.

You will forecast demand and predict spatiotemporal patterns to guide proactive planning. The role emphasizes energy-aware optimization, integration with the RaaS platform, and rigorous experimentation, validation, and