Top Remote Data Engineer Jobs in San Francisco, CA

As a Principal Machine Learning System Engineer at Atlassian, you will develop and maintain the core infrastructure for machine learning models and pipelines, tackle complex architecture challenges, lead engineering teams, and collaborate across departments to enhance AI integration and functionality.

This role involves building and integrating machine learning solutions to evaluate customer risks, support multiple financial products, optimize decision-making processes using AI/ML, and collaborate across teams to improve customer experience.

The Senior Machine Learning Engineer will design and maintain ML infrastructure, develop and deploy machine learning models, monitor performance, collaborate with teams, and mentor junior engineers at Kikoff, a consumer fintech startup focused on credit-building solutions.

As a Staff Machine Learning Engineer, you will develop ML strategies to enhance customer support, lead the development of a natural language understanding system, and collaborate with teams across the organization to leverage existing ML capabilities.

This role involves leveraging technical expertise to discover analytical opportunities, conduct diagnostic discussions, and translate complex business issues into actionable solutions. The engineer will build custom solutions, engage with enterprise clients, and implement systems to enhance operational efficiency.

As a Senior Data Engineer, you will design and build a data platform for analyzing financial markets, collaborating with traders and researchers to manage data sources and ensure data quality. You will mentor junior team members and contribute to project management, overseeing data ingestion and maintaining system reliability.

As a Data Engineer at Kustomer, you will enhance the data architecture, streamline data ingestion, and provide insights for product decisions. Responsibilities include writing tools for data classification, conducting data analysis, debugging, and ensuring data pipeline integrity, while collaborating closely with stakeholders for data visualization and best practices implementation.