Machine Learning / AI Optimization Engineer

ResponsibilitiesDevelop and deploy machine learning and optimization algorithms powering Snoonu’s hybrid autonomous logistics platform, enabling intelligent coordination across human couriers, ground robots, and drones.Design and implement advanced optimization strategies for task allocation, routing, scheduling, and dispatch decision-making across a multi‑agent fleet environment.Develop and train multi‑agent reinforcement learning (MARL) models to improve fleet‑wide coordination efficiency under dynamic demand and traffic conditions.Build demand forecasting and spatiotemporal prediction models using modern architectures (e.g., Transformers, temporal models, graph-based methods) to support proactive positioning and better resource planning.Develop energy‑aware and environment‑aware optimization models to improve battery utilization, charging schedules, delivery sequencing, and overall operational efficiency under Qatar’s climate constraints.Integrate ML optimization models into the Robotics-as-a-Service (RaaS) orchestration platform in collaboration with platform/backend engineers, ensuring reliability and low‑latency decision making.Evaluate model performance using defined metrics such as routing efficiency, ETA prediction accuracy, fleet coordination latency, and energy impact—supporting TRL advancement and pilot readiness.Implement experimentation pipelines including offline benchmarking, simulation validation, and controlled field pilot evaluation to improve model accuracy and generalization.Support continuous learning and model lifecycle management, including monitoring, retraining strategies, and mitigation of model drift using operational datasets.Document model architecture, assumptions, validation results, and experimentation methods to support R&D reporting, internal knowledge sharing, and stakeholder alignment.QualificationsBachelor’s or Master’s degree in Machine Learning, Artificial Intelligence, Computer Science, Data Science, Operations Research, or a related field. (PhD is a plus for research-driven optimisation work.)2–4 years of backend development experience, with strong hands‑on Python/C++ expertise.Strong analytical and problem‑solving skills with the ability to work on complex real‑world robotics challenges.Research‑oriented mindset and ability to translate experimentation into production‑ready autonomy improvements.Ability to work effectively in cross‑functional teams (robotics, embedded, platform/software, operations).Clear communication skills and ability to document technical work, trade‑offs, and validation outcomes.High ownership and accountability for results, timelines, and engineering quality.Adaptability to fast‑paced R&D environments involving prototyping, testing, and iterative development.Strong business context understanding, able to translate operational needs into technical solutions.Open to feedback and proactive in applying improvements suggested by senior engineers or tech leads.Core Backend & PythonStrong foundation in Python, including OOP principles, design patterns, and writing clean, maintainable code.Experience building backend services using frameworks such as FastAPI, Flask, or Django.Ability to design, develop, and maintain RESTful APIs with proper error handling and logging.AWS & CloudExperience using AWS services, such as Lambda, SQS/SNS, API Gateway, Step Functions, DynamoDB, RDS, S3, and CloudWatch.Experience designing event-driven and serverless architectures.Familiarity with IAM, environment configuration, and cloud security best practices.Embedded systems focusStrong proficiency in Python and experience using deep learning frameworks.Hands‑on experience in one or more of the following areas:Reinforcement Learning (RL) and Multi-Agent Reinforcement Learning (MARL)Optimisation and decision systems for routing, scheduling, dispatchTime-series forecasting and spatiotemporal modellingGraph Neural Networks (GNNs) for mobility and routing problemsEnergy optimisation and predictive control techniques (MPC, hybrid ML+control)Strong understanding of evaluation methodologies, including metrics design, baselines, A/B testing, and offline/online validation.Experience working with large-scale datasets, feature engineering, and model deployment constraints.Familiarity with simulation environments, digital twins, or synthetic data generation workflows is a strong plus.Knowledge of real-time systems, streaming data pipelines, and ML model monitoring practices is an advantage.Experience in logistics, last-mile delivery, fleet routing, or mobility analytics is a strong advantage.Snoonu is Qatar’s homegrown Super App, reinventing daily life with blazing-fast delivery, shopping, and more – all in one place. Powered by tech, driven by a global team, and obsessed with making life easier.To be the first Qatari Ultra App that propels the region and its community through innovation and technology. We have global ambitions where what we do surpasses norms and limitations every