Senior ML Engineer

Role Overview

Responsibilities

• Design optimization strategies for task allocation, routing, scheduling, and dispatch across a multi-agent fleet.
• Develop and train MARL models to improve fleet coordination under dynamic demand and traffic.
• Build forecasting and spatiotemporal prediction models, including Transformer, temporal, or graph-based approaches, for positioning and resource planning.
• Develop energy-aware models for battery use, charging schedules, delivery sequencing, and operational efficiency under Qatar's climate constraints.
• Integrate ML optimization models into the RaaS orchestration platform, supporting reliability and low-latency decisions.
• Evaluate routing efficiency, ETA accuracy, fleet coordination latency, and energy impact to support TRL advancement and pilot readiness.
• Run offline benchmarks, simulation validation, and controlled field-pilot evaluation.
• Monitor models, develop retraining strategies, and mitigate model drift using operational data.
• Document architectures, assumptions, validation results, and experimentation methods for R&D reporting and stakeholder alignment.

Qualifications and Experience

• Bachelor's or Master's degree in Machine Learning, Artificial Intelligence, Computer Science, Data Science, Operations Research, or a related field; a PhD is a plus for research-driven optimization work.
• 2–4 years of backend development experience and strong hands-on Python/C++ expertise.
• Strong analytical and problem-solving skills for real-world robotics challenges; able to translate experimentation into production-ready autonomy improvements.
• Able to collaborate across robotics, embedded, platform/software, and operations teams; communicate and document technical work and validation outcomes.
• Demonstrates ownership, accountability, adaptability, and openness to feedback in a fast-paced R&D environment.

Backend, Cloud, and ML Skills

• Strong Python skills, including OOP, design patterns, and clean, maintainable code; proficiency in Python and experience with deep-learning frameworks.
• Backend services experience with frameworks such as FastAPI, Flask, or Django; design and maintain RESTful APIs with error handling and logging.
• AWS experience with services such as Lambda, SQS/SNS, API Gateway, Step Functions, DynamoDB, RDS, S3, and CloudWatch.
• Experience with event-driven and serverless architectures; familiarity with IAM, environment configuration, and cloud security practices.
• Hands-on experience in one or more of RL/MARL; routing, scheduling, or dispatch optimization; time-series or spatiotemporal modeling; GNNs; energy optimization or predictive control.
• Strong understanding of metric design, baselines, A/B testing, offline/online validation, large-scale datasets, feature engineering, and model deployment constraints.
• Simulation environments, digital twins, or synthetic-data workflows are a plus; real-time systems, streaming pipelines, and model monitoring are advantageous.
• Logistics, last-mile delivery, fleet routing, or mobility analytics experience is a strong advantage.

Perks and Work-Life

• Remote and global collaboration, learning budgets, course access, and tools for growth.
• Autonomy, flexible time off, and an agile working environment.

About Snoonu