Senior Research Engineer, GenAI & Cloud AI

Google; Mountain View (CA); On-site; USD 262,000 - 364,000; Full time

Job summary

Google Mountain View, CA, USA seeks a Senior Staff Research Engineer to advance foundation models and enterprise AI within Google Cloud AI Research. You will own research directions, publish outcomes, and collaborate with product teams to translate breakthroughs into Cloud AI products.

You will tackle issues like hallucinations, retrieval-augmented LLMs, multimodal inputs, and generalization, leading cross-functional projects across research and product teams in a matrixed environment.

Qualifications

• Bachelor’s degree or equivalent practical experience.
• 8 years of experience in software development.
• 7 years leading technical project strategy, ML design, and ML infrastructure (e.g., model deployment, model evaluation, data processing, debugging, fine tuning).
• 5 years of experience with design and architecture; and testing/launching software products.
• 5 years of experience with GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with GenAI-related concepts (language modeling, computer vision).

Responsibilities

• Develop innovative ML and NLP techniques and publish outcomes at conferences.
• Collaborate with Google Cloud product teams to address customer needs and realize business impact.
• Invent and implement generalizable methods that scale across real-world AI issues.
• Engage with researchers and engineers across Google.

Skills

Software development

Technical leadership

ML design

ML infrastructure

Software architecture

GenAI techniques

Education

Bachelor's degree

Master's/PhD preferred

Job description

Google Mountain View, CA, USA seeks a Senior Staff Research Engineer to advance foundation models and enterprise AI within Google Cloud AI Research. You will own research directions, publish outcomes, and collaborate with product teams to translate breakthroughs into Cloud AI products.

You will tackle issues like hallucinations, retrieval-augmented LLMs, multimodal inputs, and generalization, leading cross-functional projects across research and product teams in a matrixed environment.