Application Open:
Full-Time
Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) is seeking a Senior Full Stack Engineer to build end-to-end data solutions for cutting-edge robotics and AI research, joining MBZUAI’ Robot Learning Laboratory, a state-of-the-art core facility. This role is ideal for candidates who have a strong background in data and computer engineering, with a passion to build end-to-end data solutions for cutting-edge robotics and AI research.
With an emphasis on practical implementation complemented by research-driven innovation, this position focuses on designing and developing data pipelines, APIs, and user interfaces that enable researchers to efficiently collect, process, analyze, and visualize robotics data at scale. This role requires strong backend and frontend skills, with the ability to rapidly deliver production-quality features in a fast-paced research environment.
Key Responsibilities
Data Pipeline Development
- Design and implement scalable ETL/ELT pipelines for robotics data (sensor streams, simulation outputs, model artifacts)
- Build real-time and batch data processing workflows using Spark, Flink, or similar frameworks
- Develop data ingestion connectors for diverse sources (ROS2 topics, S3 buckets, databases, APIs)
- Implement data transformation logic with proper error handling, retry mechanisms, and monitoring
- Optimize pipeline performance for high-throughput, low-latency data processing
- Design data quality checks and validation frameworks (Great Expectations, custom validators)
- Implement data versioning and lineage tracking for reproducibility
Backend API Development
- Design and implement RESTful APIs using FastAPI, Flask, or Django
- Build GraphQL APIs for flexible data querying and aggregation
- Develop authentication and authorization systems (OAuth2, JWT, RBAC)
- Implement API rate limiting, caching (Redis), and performance optimization
- Design database schemas and optimize queries for PostgreSQL, MongoDB, or similar databases
- Build asynchronous task processing systems (Celery, RQ) for long-running operations
- Implement comprehensive API documentation (OpenAPI/Swagger) and testing (pytest)
Frontend Development
- Build responsive web applications using React, Vue.js, or Angular
- Develop data visualization dashboards using D3.js, Plotly, or similar libraries
- Create interactive data exploration tools for robotics datasets (3D point clouds, video streams, time-series)
- Implement real-time data streaming interfaces using WebSockets or Server-Sent Events
- Design intuitive user interfaces following UX best practices and accessibility standards
- Optimize frontend performance (lazy loading, code splitting, caching)
- Implement state management (Redux, Vuex) for complex applications
Data Warehouse and Analytics
- Design dimensional models (star/snowflake schemas) for analytics use cases
- Build data marts and aggregation tables for reporting and dashboards
- Implement incremental data loading and change data capture (CDC) patterns
- Develop SQL-based analytics queries and optimize for performance
- Create materialized views and summary tables for fast query response
- Integrate with BI tools (Tableau, Superset, Metabase) for self-service analytics
System Integration and Orchestration
- Integrate with external systems (cloud storage, compute clusters, ML platforms)
- Design workflow orchestration using Airflow, Prefect, or Dagster
- Implement event-driven architectures using Kafka, RabbitMQ, or similar message brokers
- Build data catalog and metadata management systems (DataHub, Amundsen)
- Develop CLI tools and SDKs for programmatic access to data platform
DevOps and Production Support
- Write Infrastructure as Code (Terraform, Ansible) for application deployment
- Implement CI/CD pipelines for automated testing and deployment
- Set up monitoring, logging, and alerting for data pipelines and APIs
- Troubleshoot production issues and optimize system performance
- Participate in on-call rotation for critical data services
- Document architecture, APIs, and operational procedures
Collaboration and Mentorship
- Work closely with robotics researchers to understand data requirements
- Collaborate with ML engineers on feature engineering and model training pipelines
- Conduct code reviews and provide technical guidance to junior engineers
- Share knowledge through technical presentations and documentation
- Contribute to architectural decisions and technology evaluations.
Academic Qualifications
- Master degree in Robotics, Physics, Electrical Engineering, Mechanical Engineering, Control Systems, Aerospace Engineering, Design Engineering, Computer Science or a related field.
- A PhD will be preferred.
Professional Experienced Required
Essential:
Backend Proficiency
- Languages: Expert-level Python; proficiency in Go, Java, or Scala is a plus; C++ for high-performance data processing and robotics integration
- Frameworks: Strong experience with FastAPI, Flask, or Django; async programming (asyncio, aiohttp)
- Databases: Deep knowledge of PostgreSQL or MySQL; experience with NoSQL (MongoDB, Cassandra, Redis)
- Data Processing: Hands-on experience with Spark, Flink, or Pandas for large-scale data processing
- APIs: Proven ability to design and implement RESTful and GraphQL APIs with proper authentication
- Message Queues: Experience with Kafka, RabbitMQ, or similar for event-driven architectures
Frontend Proficiency
- Web Frameworks: Proficiency in React, Vue.js, or Angular with modern JavaScript (ES6+)
- Desktop Applications: Experience with Qt/QML or Electron for cross-platform desktop tools and data visualization applications
- Languages: Strong TypeScript skills; understanding of HTML5, CSS3, and responsive design; C++ for Qt-based applications
- Visualization: Experience with D3.js, Plotly, ECharts, or similar data visualization libraries
- State Management: Familiarity with Redux, Vuex, or similar state management patterns
- Build Tools: Experience with Webpack, Vite, or similar bundlers; npm/yarn package management; CMake for C++ projects
Data Engineering
- Orchestration: Experience with Airflow, Prefect, or Dagster for workflow management
- Data Quality: Familiarity with data validation frameworks (Great Expectations, Deequ)
- SQL: Advanced SQL skills including window functions, CTEs, and query optimization
- Data Modeling: Understanding of dimensional modeling, normalization, and denormalization strategies
DevOps
- Containers: Proficiency with Docker and Kubernetes for application deployment
- CI/CD: Experience with GitLab CI/CD, GitHub Actions, or Jenkins
- Cloud: Hands-on experience with AWS, Azure, or GCP (S3, EC2, RDS, Lambda)
- Monitoring: Familiarity with Prometheus, Grafana, ELK stack for observability
Preferred Experience:
- Robotics Domain: Experience with ROS/ROS2, sensor data processing (LiDAR, cameras, IMU), or robotics simulation
- 3D Visualization: Experience with Three.js, WebGL, or point cloud visualization tools
- ML Pipelines: Familiarity with MLflow, Kubeflow, or feature stores (Feast, Tecton)
- Streaming: Experience with real-time data streaming (Kafka Streams, Flink, Spark Streaming)
- Search: Experience with Elasticsearch or similar search engines
- Graph Databases: Familiarity with Neo4j or similar for lineage and relationship modeling
- Open Source: Contributions to relevant open-source projects (Airflow, Spark, React)
- Scale: Experience processing TB-scale datasets or handling high-throughput APIs (1000+ RPS)
- Testing: Strong testing practices (TDD, integration testing, end-to-end testing)
