Top Tech Jobs & Startup Jobs in San Francisco Bay Area, CA

The Applied AI Researcher will explore new AI system architectures, develop prototypes, analyze data, and redefine human-AI collaboration for enterprise applications.

Researcher will adapt foundation models for real-world applications, focusing on performance, alignment, and human-centric objectives to optimize AI systems for enterprises.

The Applied AI Researcher will design complex AI systems, integrate diverse components, and innovate in the utilization of AI for enterprise workflows. They will also conduct research on system interactions and workflows while building prototypes and demonstrating AI capabilities.

As an AI Strategist, you'll lead customer projects, embedding in workflows to identify AI opportunities, ensuring delivery quality, user adoption, and customer trust.

The Applied AI Researcher will design benchmark frameworks, conduct statistical evaluations, and leverage AI to redefine enterprise software usage and measure intelligent system performance.

As an AI Platform Engineer, design and optimize the AI-native platform, develop scalable infrastructure, and collaborate with teams to solve complex AI deployment challenges.

As a Product Marketing Manager, you'll craft Distyl's market narrative, create sales enablement materials, and drive customer engagement through effective content and insights, collaborating closely with sales, engineering, and leadership teams.

The Applied AI Researcher will design self-improving systems that evaluate and enhance performance through feedback and self-modeling. The role demands a creative approach to redefine software usage, focusing on continuous learning and improvement using advanced AI techniques.

Senior technical leader responsible for the success of complex enterprise AI deployments: setting architecture and evaluation strategy, ensuring production reliability, coaching and developing engineering leads, partnering with customer executives, debugging critical issues, and scaling pilots to enterprise deployments while using AI-native engineering practices.

Build and run post-training workflows to improve AI system behavior and reliability. Develop datasets, reward models, fine-tuning and evaluation pipelines, run experiments, analyze failure modes, and create repeatable processes for deploying robust, measurable AI systems. Partner with researchers, engineers, and customers to apply improvements in production and communicate results and tradeoffs.