Top Hybrid Machine Learning Engineer Jobs in San Francisco, CA

Lead the ML/AI function, architect multi-modal AI systems, define evaluation frameworks, drive productionization of ML systems, and mentor a high-performing ML team.

The Senior Machine Learning Engineer will develop and optimize ML infrastructure, create data pipelines, and model orchestration ensuring data quality and performance monitoring.

The role involves analyzing datasets, designing feedback loops, and improving ML models in AI-based contract management systems. Responsibilities include data evaluation, collaboration with engineers and PMs, and supporting the development of data infrastructure.

The Senior Machine Learning Engineer will develop and maintain ML infrastructure, deploy models, collaborate with teams, and mentor junior engineers.

As a Senior Machine Learning Engineer, you will develop AI systems in high-stakes environments, ensuring reliability and customer impact. You will improve production ML systems, optimize models, and collaborate with research teams to deploy solutions that enhance productivity in complex domains.

The Applied ML Engineer will productionize and scale machine learning systems for a voice AI platform, optimizing deployment and performance in healthcare environments.

The Staff Software Engineer will develop and deploy high-performance machine learning systems, optimizing large language models for production environments. Responsibilities include ensuring high availability and low latency for AI applications and working collaboratively across teams.

As a Senior Machine Learning Engineer, you will own the ML stack, develop generation systems, improve quality, collaborate cross-functionally, and build data pipelines.

The Software Engineer will lead the development of Baseten Voice AI, focusing on real-time model serving systems, collaboratng with teams on architecture and implementation.

The Machine Learning Researcher will develop new architectures for data-efficient model induction, focusing on optimizing generalization performance and runtime inference in networks, while contributing to a performance-driven code base.