Principal M-LLM Post-Training & Real-Time Inference Engineer

Google Inc.; San Jose (CA); On-site; USD 307,000 - 427,000; Full time

Benefits offered by this job

Equity

Bonus target

Benefits

Job summary

Google in San Jose, CA, is seeking a Principal M-LLM Post-Training and Execution Software Engineer, XR to lead production-ready features from state-of-the-art models for the XR platform.

You will bridge model alignment and high-performance execution, ensuring safety, reliability, and optimization for real-time XR demands while enabling devices to perceive and understand the environment at scale.

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.

Responsibilities

• Design and scale post-training infrastructure for Multimodal LLMs including instruction tuning, RLHF, and evaluation frameworks.
• Architect and optimize real-time M-LLM inference engines for XR hardware.
• Lead data pipelines for vision and multimodal understanding to improve spatial context modeling.
• Collaborate with Product and UX to define intelligence primitives for spatial apps.
• Set safety and alignment standards for XR-specific AI deployment at scale.

Skills

Go-to-market strategy

Leadership

Large-scale ML systems

Education

Bachelor’s degree in Computer Science, Mathematics, or equivalent practical experience

Job description

Google in San Jose, CA, is seeking a Principal M-LLM Post-Training and Execution Software Engineer, XR to lead production-ready features from state-of-the-art models for the XR platform.

You will bridge model alignment and high-performance execution, ensuring safety, reliability, and optimization for real-time XR demands while enabling devices to perceive and understand the environment at scale.