Top AI Research Jobs in San Francisco Bay Area, CA

Assist the research team in developing and evaluating domain adaptation methods for LLMs and AI systems focused on enterprise domains.

As an AI Research Engineer, you'll conduct research in AI and automated reasoning, creating innovative solutions, testing systems, and contributing to scientific publications while collaborating with engineering teams.

The Quantum Error Correction Software Engineer will research quantum error correcting codes, design fault-tolerant quantum computations, and translate research into production-ready code.

As an AI Researcher, you will develop novel architectures and optimizations in machine learning, collaborating with teams to enhance open models and ensuring cutting-edge research practices.

As a Research Scientist, design, implement, and validate AI techniques, prototype systems, and convert research into practical applications while collaborating with industry partners.

The role involves owning the product vision for AI-enabled capabilities in clinical development. Responsibilities include stakeholder engagement, defining product requirements, prioritizing backlogs, and ensuring compliance with regulations while driving user adoption and continuous improvement.

As a Research Engineer, you'll enhance AI models through post-training and evaluation pipelines, focusing on data quality and performance metrics.

Design and develop large-scale multimodal transformer models that fuse camera, LiDAR, and radar. Research cross-modal attention and token-fusion strategies, build scalable distributed training pipelines, explore self-supervised/contrastive pretraining, and optimize models for real-time, low-latency inference.

The AI Growth Architect will tackle growth challenges in the fashion industry, translating insights into actionable strategies and directly impacting customer growth using Hilbert's AI capabilities.

The Post-Training Research Scientist will focus on both pure and applied research in post-training methodology and inference, designing experiments, publishing findings, and collaborating with engineering teams to advance machine learning models.