Top Remote Artificial Intelligence Data Engineering Jobs in San Francisco, CA

The role involves architecting intelligence infrastructure, building data pipelines, collaborating with other teams, and ensuring data accessibility and security for threat detection systems.

Serve as an AI/ML-focused solution engineer for Healthcare & Life Sciences: provide technical guidance, design cloud data and lakehouse architectures on Snowflake, lead demos and proofs-of-concept, support sales cycles, collaborate with product and engineering, and communicate AI/ML value to technical and executive stakeholders.

Design, build, and maintain scalable data pipelines and tooling to support AI and analytics. Manage datastores (MySQL, Redshift, OpenSearch), implement AWS-based ETL/ELT solutions, collaborate with data science and product teams, and ensure data quality, automation, and production reliability.

The Engagement Manager will oversee the data modernization lifecycle, coordinating with partners to implement modern data platforms and manage multiple concurrent engagements effectively.

As an AI Engineer, you'll work closely with customers to understand their needs, train and deploy models, and build scalable AI systems.

Lead risk modeling initiatives from data exploration to deployment, defining methodologies, advising clients, and integrating models into platforms.

The Senior Quantitative Analyst is responsible for building data pipelines, conducting Monte Carlo simulations, and converting complex Excel models to code while communicating effectively with stakeholders.

Lead the Core Analytics team, manage data engineering standards, design analytics architectures, and mentor team members while ensuring data integrity and analytics effectiveness.

The Facets Systems Analyst will perform development and maintenance tasks in Facets, lead a team of developers, and ensure quality outcomes through testing methodologies while maintaining compliance with healthcare standards.

As a Data Engineer, you will build and optimize data pipelines, manage ETL workflows, and ensure efficient data access and quality within the infrastructure.