Explore cutting-edge Research Engineer, Data Infrastructure jobs and discover a career at the intersection of large-scale data systems and advanced research. Professionals in this role are the foundational architects who build and maintain the robust data ecosystems that power modern AI, machine learning, and scientific discovery. Their core mission is to transform raw, often massive and complex data streams into organized, accessible, and high-quality resources that accelerate model development and innovation. A Research Engineer in Data Infrastructure typically focuses on designing and implementing scalable data pipelines and storage solutions. This involves creating efficient ETL (Extract, Transform, Load) processes to ingest, clean, and structure data from diverse sources, ensuring it is readily queryable and primed for training complex algorithms. A key responsibility is optimizing the entire data lifecycle—from collection and upload logic at the edge (like sensors or devices) through to processing in on-premise clusters or cloud environments. They build the "data engine" that ensures seamless flow and integrity of information across distributed systems. Common responsibilities in these roles include developing tools and platforms for data management, such as systems for automated data validation, versioning, and lineage tracking. These engineers often create front-end applications for data visualization, exploration, and annotation, enabling research teams to interact with and label datasets efficiently. A significant part of the role may also involve applying machine learning techniques themselves to automate dataset curation, organization, and labeling tasks, thereby improving the overall quality and speed of research cycles. Typical skills and requirements for Research Engineer, Data Infrastructure jobs include strong software engineering proficiency, particularly in languages like Python, Scala, or Go, coupled with deep expertise in distributed data processing frameworks (e.g., Apache Spark, Beam) and storage technologies (e.g., SQL/NoSQL databases, data lakes). Experience with cloud platforms (AWS, GCP, Azure) and containerization (Docker, Kubernetes) is highly valued. A successful candidate usually possesses a blend of systems design knowledge to architect reliable infrastructures and a solid understanding of the research workflow to align technical solutions with scientific needs. This profession is ideal for those who excel at solving complex engineering challenges to enable groundbreaking research, making these roles critical in organizations pushing the boundaries of technology.
