Principal M-LLM Post-Training and Execution Software Engineer, XR

• Design and scale the post-training infrastructure for Multimodal LLMs, including instruction tuning, RLHF, and automated evaluation frameworks.
• Architect and optimize the execution engines that support real-time M-LLM inference on XR hardware, balancing high-fidelity output with battery-conscious processing.
• Lead the development of large-scale computer vision and multimodal data pipelines to improve model understanding of spatial and environmental contexts.
• Partner with Product and UX teams to define the "intelligence" primitives that will power the next generation of spatial applications.
• Set the standard for model safety and alignment for XR-specific use cases, ensuring responsible AI deployment at scale.

Minimum qualifications:

• Bachelor’s degree in Computer Science, Mathematics, or equivalent practical experience.
• 15 years of experience in software engineering, specifically in large-scale machine learning systems or computing.
• Experience driving the go-to-market strategy for AI-driven technologies.
• Experience leading engineering teams through the full execution cycle of AI products.

Preferred qualifications:

• Experience with high-performance inference engines and custom AI accelerators.
• Advanced knowledge of computer vision pipelines, 3D perception, and their integration with large-scale language models.
• Distinctive problem-solving and analytical skills applied to the challenges of real-time spatial computing.
• Specialized expertise in large-scale post-training workflows, including Reinforcement Learning from Human Feedback (RLHF) and fine-tuning at scale.

• Bachelor’s degree in Computer Science, Mathematics, or equivalent practical experience.
• 15 years of experience in software engineering, specifically in large-scale machine learning systems or computing.
• Experience driving the go-to-market strategy for AI-driven technologies.
• Experience leading engineering teams through the full execution cycle of AI products.