AI​/Autonomous Systems Engineer

What You’ll Get Your Hands On

• Develop and implement autonomous perception, localisation, and navigation algorithms for ground robots and aerial drones within Snoonu’s AI‑driven autonomous logistics platform.
• Design and optimise multi‑sensor perception pipelines using LiDAR, radar, RGB/depth cameras, IMU, and GPS to ensure robust operation in Qatar’s challenging conditions (heat, dust, glare, reflective surfaces).
• Develop and improve object detection, free‑space segmentation, tracking, and obstacle prediction modules to support safe real‑world navigation.
• Implement localisation and mapping capabilities, including Visual‑Inertial SLAM, sensor‑fusion‑based localisation, and GPS‑assisted fallback mechanisms.
• Build simulation‑based testing workflows to validate autonomy performance under realistic Qatari environments, traffic patterns, and operational constraints.
• Collaborate with robotics, embedded, and cloud teams to ensure seamless integration of perception outputs into motion planning, control, fleet management, and RaaS orchestration layers.
• Evaluate model performance using defined validation metrics (accuracy, latency, robustness), and contribute to iterative improvements to achieve TRL advancement targets.
• Support dataset collection, labelling strategies, and pipeline improvements to enable continuous model training and validation from real‑world deployments.
• Participate in on‑site experiments, field testing, and pilot demonstrations, providing debugging and performance tuning of autonomy modules.
• Document model designs, experimental results, and validation procedures to support R&D reporting, regulatory compliance, and knowledge‑transfer activities.

• Bachelor’s or Master’s degree in Artificial Intelligence, Robotics, Computer Vision, Computer Engineering, or a related field. (PhD is a plus for research‑heavy autonomy 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.

• Strong 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.

• Experience using AWS services, such as Lambda, SQS/SNS, API Gateway, Step Functions, Dynamo
  
  DB, RDS, S3, and Cloud Watch.
• Experience designing event‑driven and serverless architectures.
• Familiarity with IAM, environment configuration, and cloud security best practices.

• Strong proficiency in Python and experience using deep-learning frameworks.
• Experience in one or more autonomy domains:
  • Computer Vision and Perception Pipelines
  • Sensor fusion
  • Localisation and mapping (SLAM / Visual‑Inertial Odometry)
  • Object detection / tracking / segmentation
  • Motion planning support systems
• Familiarity with robotics development environments such as ROS / ROS2 and real‑time robotics data pipelines.
• Experience working with LiDAR point clouds, camera streams, and IMU‑based motion data.
• Ability to optimise ML/AI systems for real‑time performance and edge deployment constraints.
• Experience with simulation tools or autonomy test environments is a strong plus (e.g., Gazebo, CARLA, Air Sim, Isaac Sim).
• Knowledge of safe autonomy principles, anomaly detection, or human‑in‑the‑loop safety mechanisms is a plus.

Apply now to join a team where your contributions spark change and your voice is heard. Let’s make some magic together.