Minimum qualifications:

• Bachelor’s degree or equivalent practical experience.
  
• 2 years of experience with software development in Python or C++.
• 1 year of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging).
• Experience with GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with GenAI-related concepts (language modeling, computer vision).

Preferred qualifications:

• Master's degree or PhD in Computer Science, or a related technical field.
• 2 years of experience with data structures and algorithms.
• Experience conducting applied research to enable new functionality and improve the quality and efficiency of large language and multimodal models.
• Knowledge of machine learning and statistics.

About the job

Our team is at the forefront of building the next generation of conversational AI. We're developing agentic AI solutions for smart glasses, utilizing Gemini Live and Astra to create a unique and trusted multimodal experience. This technology delivers instant, natural conversational intelligence directly to the user's eye, allowing them to navigate their world more immersively than ever.

In this role, you will design multimodal agentic solutions focused on goal-oriented reasoning tasks. You will enhance and develop new multimodal tools and extensions. You will define and execute the strategy for data, evaluation, and post-tuning of the Gemini model to enhance its impact for smart glasses use cases.

Responsibilities

• Design, develop, and deploy scalable and agentic AI solutions for high-value, real-world multimodal conversational AI use cases on smart glasses.
• Gain an understanding of the Gemini Live and Astra tech stack and infrastructure. Optimize agent architecture/orchestration to ensure efficient deployment and operation at scale, with a focus on inference cost optimization.
• Take ownership of AI quality for production systems. This includes defining technical metrics, implementing evaluation frameworks, analyzing loss patterns, and driving improvements through data collection and smart data generation and model enhancements.
• Implement, optimize, and advance AI techniques, with a focus on multimodal conversational quality, multimodal tool use, and multimodal goal-oriented reasoning.