Research Engineer — Generative Conversational AI & LLMs

Google Inc.; New York (NY); On-site; USD 207,000 - 300,000; Full time

Job summary

Google DeepMind is seeking a Research Engineer to advance conversational AI through large-scale experimentation and rapid deployment of innovative ideas. You will work across research programs and internal projects to push the boundaries of multimodal dialogue systems.

The role emphasizes building and evaluating AI/ML features, leveraging NLP and LLM techniques, and collaborating with industry and academia to publish and iterate on state-of-the-art models.

Qualifications

• Bachelor’s degree in Computer Science, Machine Learning, Mathematics, Cognitive Science, or a related technical field, or equivalent practical experience.
• 8 years of experience in data preparation, training, and evaluation of ML models.
• Experience building or implementing AI/ML-driven features or infrastructure (e.g., working with Large Language Models (LLMs), NLP, or data pipelines).
• Experience in Machine Learning, Artificial Intelligence, AI algorithms, and data analysis.

Responsibilities

• Set up large-scale tests and deploy promising ideas quickly and broadly.
• Design experiments and prototype implementations; explore new architectures.
• Publish and collaborate with academia and research partners to advance AI methods.
• Partner with Gemini or DeepMind teams to design, develop, and deploy multimodal conversational agents.
• Develop audio-first models capable of orchestrating and planning complex dialogs, including external tools like search.

Skills

Data preparation

ML model training

Model evaluation

NLP/LLMs

AI algorithms

Education

Bachelor's degree in CS/ML or related field

Job description

Google DeepMind is seeking a Research Engineer to advance conversational AI through large-scale experimentation and rapid deployment of innovative ideas. You will work across research programs and internal projects to push the boundaries of multimodal dialogue systems.

The role emphasizes building and evaluating AI/ML features, leveraging NLP and LLM techniques, and collaborating with industry and academia to publish and iterate on state-of-the-art models.