ML Engineer: Multimodal Perception & Authentication

OpenAI; San Francisco (CA); Hybrid; USD 342,000 - 399,000; Full time

Benefits offered by this job

Relocation assistance

Hybrid work model

Job summary

OpenAI in San Francisco is seeking a Machine Learning Engineer focused on multimodal perception and authentication. You will advance perception models that fuse visual and audio signals, work with larger multimodal models, and collaborate with hardware, firmware, software, and product teams to ship research as real-world systems.

The role emphasizes cross-disciplinary collaboration, privacy-aware applications, and the ability to design robust experiments and evaluations.

Qualifications

• Strong background in computer vision, audio or speech ML, multimodal learning, or sensing.
• Experience developing specialized ML models or larger multimodal models.
• Experience bringing research ideas into practical systems or prototypes.
• Ability to design experiments, build evaluations, and analyze model behavior.

Responsibilities

• Research and develop multimodal perception and authentication methods across visual, audio, and sensing signals.
• Explore how specialized perception models and larger multimodal models can work together.
• Design data, training, and evaluation approaches for 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.
• Collaborate with hardware, firmware, software, and product teams to deploy research as working systems.

Skills

Computer vision

Audio ML

Multimodal learning

Sensing

Tools

Python

PyTorch

C++

Job description

OpenAI in San Francisco is seeking a Machine Learning Engineer focused on multimodal perception and authentication. You will advance perception models that fuse visual and audio signals, work with larger multimodal models, and collaborate with hardware, firmware, software, and product teams to ship research as real-world systems.

The role emphasizes cross-disciplinary collaboration, privacy-aware applications, and the ability to design robust experiments and evaluations.