Manager, Multi-Modal Language Action Models
Zoox - Foster City, CA
Posted Nov 4, 2025
Benefits
- Parental leave
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- Non-birth-parent leave
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- Family-building benefits
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- Fertility benefits: Not verified
- Adoption assistance: Not verified
- Surrogacy assistance: Not verified
- Mental health support
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- Relocation assistance
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- Childcare support
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- Learning budget
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- 401(k) match
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Schedule
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- Weekend work
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Application
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About this role
Manager, Multi-Modal Language Action Models Foster City, CA Zoox is developing the first fully autonomous, purpose-built electric vehicle designed for autonomous ride-hailing. We are a passionate, collaborative, and fast-moving team working to build a revolutionary new mobility experience. This role offers the opportunity to make critical contributions to a challenging and groundbreaking product. Zoox is seeking an experienced Manager of Multi-Modal Language Action Models to lead a team focused on applying cutting-edge Large Multi-Modal Models (MLLMs) to solve concrete, offline autonomy problems. This role is crucial for deploying state-of-the-art AI into our autonomous driving platform, directly impacting the safety and performance of our technology. Zoox is developing the first fully autonomous, purpose-built electric vehicle designed for autonomous ride-hailing. We are a passionate, collaborative, and fast-moving team working to build a revolutionary new mobility experience. This role offers the opportunity to make critical contributions to a challenging and groundbreaking product. Zoox is seeking an experienced Manager of Multi-Modal Language Action Models to lead a team focused on applying cutting-edge Large Multi-Modal Models (MLLMs) to solve concrete, offline autonomy problems. This role is crucial for deploying state-of-the-art AI into our autonomous driving platform, directly impacting the safety and performance of our technology. In this role, you will: - Leadership: Lead and manage a team of talented Machine Learning Engineers specializing MLLMs for concrete applications. - Model Development & Deployment: Oversee the full lifecycle of MLLM development, with a specific focus on post-training methodologies (e.g., fine-tuning, RLHF, context engineering) to optimize models for
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