Top Remote Data Engineer Jobs

As a Senior Machine Learning Engineer, you will research, develop, deploy, and monitor ML models to enhance patient engagement and outcomes. You will collaborate with software engineers to create scalable software and infrastructure, translating business objectives into machine learning problems and analyzing user data to optimize treatment processes.

As an Analytics Engineer at Rec Room, you will support the data warehouse and analytics layer, develop ELT pipelines, design dashboards, and collaborate with various teams on data modeling and analysis, while ensuring data quality and scaling infrastructure.

The BI Data Engineer will build data pipelines and Python-based ETL tools, develop and optimize data models, generate end-to-end reporting solutions, and collaborate with business leaders to define key metrics and reporting requirements, aiming to improve data usage across the company.

As a Staff Software Engineer, you will develop scalable data storage and processing systems, mentor engineers, shape product strategy, and ensure high-quality engineering processes. Key responsibilities include architecting large-scale systems and optimizing data pipelines for efficiency and performance.

As a Data Engineer at StackAdapt, you will design and implement modular and scalable real-time data pipelines, work on custom ML algorithms, and manage microservices for training and monitoring ML models in a collaborative environment.

The Head of Data will shape the data strategy at Gemini, build a high-performance data team, manage data infrastructure, optimize data warehousing, and drive data-driven initiatives across the organization. Responsibilities include overseeing data management, analytics, and collaborating with executive leadership on data integration into business strategies.

The Applied Scientist will leverage advancements in AI/ML, particularly Large Language Models and Computer Vision, to enhance legal and financial tech solutions. Key responsibilities include finetuning and deploying state-of-the-art models, building ETL pipelines, and collaborating with cross-functional teams to improve AI quality in production systems.