Machine Learning Engineer, Multimodal Perception and Authentication

OpenAI•San Francisco, CA

8d•$342,000 - $399,000•Hybrid

About The Position

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. 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.

Requirements

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 experiments, build evaluations, and investigate model behavior.
Have worked with sensing hardware, real-time systems, or other deployment constraints.
Are proficient in Python and PyTorch and comfortable with C++ or systems integration.
Have experience with authentication, biometrics, or other privacy-sensitive applications.
Enjoy working across disciplines on research problems that are still taking shape.

Responsibilities

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.