Summary 

Singapore's Embodied AI Centre (EAIC) is a new national initiative under the RIE2030 Manufacturing, Trade and Connectivity domains to establish Singapore as a leading robotics and embodied AI hub. EAIC brings together research and industry partners to build engineering and R&D capabilities, advance trusted evaluation, benchmarking and validation of embodied AI models and robotics solutions, and promote an open and interoperable ecosystem that accelerates adoption in real-world applications. 

As a Data Engineer, you will build and operate the data infrastructure and pipelines that turn raw sensor and demonstration data into training-ready datasets, thereby enabling AI researchers and robotics engineers to process and analyse large volumes of data generated and feed them for model training.  

Job Description 

• Design, build, scale and maintain data pipelines that ingest, transform and manage data from embodied AI systems, experiments and operational environments. 

• Develop and operate data platforms, storage solutions and datasets that support AI research, analytics and model development. 

• Collaborate with AI researchers, robotics engineers and software engineers to define data requirements and improve data accessibility. 

• Build and maintain data quality and curation systems, including auto-labeling pipelines. 

• Implement data quality, governance and monitoring processes to ensure reliable and trusted datasets. 

• Build and operate distributed processing infrastructure. 

• Optimise data workflows for scalability, performance and cost efficiency. 

• Support the integration of data across multiple systems, applications and cloud environments. 

• Debug production issues in data pipelines, including data corruption, schema drift, etc.  

Job Requirements 

• 2+ years of data engineering experience, with exposure to data pipelines, data engineering, and backend systems 

• Demonstrated ability to build and maintain data pipelines, ETL/ELT workflows or data platforms. 

• Strong programming fundamentals in Python, with the ability to write performant, production-grade data processing code. 

• Experience with at least one distributed data processing framework (e.g., Spark, Ray, Dask, or Flink), or strong fundamentals and willingness to ramp up quickly. 

• Ability to design data models and transform raw data into consumable datasets for analytics or applications. 

• Clear communicator who collaborates well with research/modeling partners and more senior teammates. 

• Experience with workflow orchestration tools (e.g., Airflow, Dagster, Prefect) is a plus. 

• Experience with streaming ingestion for high-volume, near-real-time data is a plus. 

• Experience with robotics- or embodied-AI-specific data (multi-embodiment datasets, teleoperation/demonstration data, egocentric video) is a plus.