OpenAISan Francisco, CAAug 3

Researcher, Multimodal Safety

Conducts research to improve the safety of multimodal AI systems spanning text, vision, and audio. The role requires experience building multimodal models, post-training frontier systems, designing safety evaluations, and translating research findings into reliable model behavior.

295k – 445k/yr

HybridAI Research

About the role

Responsibilities

Define and advance multimodal safety research for text, vision, and audio, connecting perception and semantic understanding to safe model behavior.
Build training and evaluation methods for vision-language models (VLMs), including post-training, safety evaluations, and interventions that help models respond safely and appropriately in varied contexts.
Collaborate with research, product, and model teams to translate research into safer ambient, embedded, and personalized multimodal experiences.

Requirements

Track record of building or advancing multimodal models, with depth in vision-language models, video understanding, image generation, audio, or multimodal reasoning.
Fluency across both perception and language.
Understanding of multimodal systems end to end, including encoders, projection layers, modality fusion, cross-modal reasoning, scaling, and inference tradeoffs.
Experience improving frontier model behavior through post-training using methods such as supervised fine-tuning, reinforcement learning, data curation, synthetic data, evaluation, and rigorous error analysis.
Strong research and engineering judgment for open-ended safety problems, including forming testable hypotheses, designing experiments and evaluations, diagnosing model failures, and translating findings into robust improvements.

Compensation and Benefits

Hybrid work model with 3 days in the office per week.
Relocation assistance is available to new employees.

Skills

vision-language modelsmultimodal reasoningComputer Visionnatural language processingvideo understandingimage generationaudio processingpost-trainingsupervised fine-tuningReinforcement Learningsynthetic datamodality fusioncross-modal reasoningModel Evaluationerror analysis

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