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Machine Learning Engineer, Multimodal Perception and Authentication

Openai San Francisco 1mo ago
FullTime
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About Openai (Wikipedia Record):

OpenAI is an American artificial intelligence (AI) public benefit corporation (PBC) headquartered in San Francisco, California. It develops proprietary generative AI models, particularly its generative pre-trained transformer (GPT) series of large language models. Its release of ChatGPT in November 2022 has been credited with catalyzing the AI boom; as of September 2026, ChatGPT is the fifth-most-visited website globally. OpenAI also releases the GPT Image models and Codex, an AI coding agent. In March 2026, OpenAI closed a funding round with a post-money valuation of US$852 billion, making it one of the most valuable AI pure play companies in the world, rivalled only by Anthropic.

About the Role

About the Team The Future of Computing Research team is an applied research team within OpenAI’s Consumer Devices group. We study how AI systems perceive people and their surroundings, and we turn that research into capabilities for future products.   Our work spans machine learning, sensing, and hardware, with a focus on building systems that work beyond controlled environments.   About the Role We’re looking for a machine learning engineer to help shape how future AI systems understand the physical world and the people in it. The role focuses on multimodal perception and authentication, bringing together signals from cameras, microphones, and other sensors.   You’ll work with specialized perception models and larger multimodal models, and partner with hardware, firmware, software, and product teams to bring new research into real-world systems.   This role is based in San Francisco. We work in the office three days per week and offer relocation assistance.   In this role, you will: - Research and develop multimodal perception and authentication methods across visual, audio, and other sensing signals. - Explore how specialized perception models and larger multimodal models can work together. - Design data, training, and evaluation approaches that improve performance in real-world conditions. - Study model behavior, robustness, and failure modes across sensing, data, and deployment environments. - Integrate and validate new capabilities in real-time or resource-constrained systems. - Work with hardware, firmware, software, and product teams to turn research into working systems.   You might thrive in this role if you: - Have a strong background in computer vision, audio or speech machine learning, multimodal learning, or sensing. - Have experience developing specialized machine learning models, larger multimodal models, or both. - Have brought research ideas into practical systems, prototypes, or products. - Know how to design exper

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