Responsible AI Research Engineer

DeepMind Technologies Limited; New York (NY); Hybrid; USD 207,000 - 300,000; Full time

Job summary

Google DeepMind is seeking a research-focused Software Engineer embedded across projects to design and deploy AI-driven experiments at scale. You will collaborate with researchers and engineers to push the state of the art in Responsible AI while aligning with Google's broader research ecosystem.

Responsibilities include prototyping architectures, mentoring teams, and communicating complex trade-offs to leadership, with an emphasis on scalable, high-quality code and governance for AI systems.

Qualifications

• Bachelor's degree in CS, Math, a related technical field, or equivalent practical experience.
• 8 years of experience in Python programming.
• Experience with generative AI, generative AI agents, NLP, and large language models (LLMs).
• Experience with Cloud AI/ML systems and tools (e.g., Kaggle, TensorFlow, PyTorch).

Responsibilities

• Design, prototype, scale engineering solutions, and run experiments to address emerging research priorities within the Responsibility portfolio.
• Work with Research Scientists to advance the state of the art in Responsible AI.
• Act as a technical anchor for the team, establishing best practices for code quality, scalability, and system design.
• Present complex engineering trade-offs and research results clearly to cross-functional stakeholders and leadership.
• Lead project teams and set technical direction.

Skills

Python

Generative AI

NLP

LLMs

Cloud AI/ML

Education

Bachelor's degree in Computer Science, Math, or related field

Tools

Kaggle

TensorFlow

PyTorch

Job description

Google DeepMind is seeking a research-focused Software Engineer embedded across projects to design and deploy AI-driven experiments at scale. You will collaborate with researchers and engineers to push the state of the art in Responsible AI while aligning with Google's broader research ecosystem.

Responsibilities include prototyping architectures, mentoring teams, and communicating complex trade-offs to leadership, with an emphasis on scalable, high-quality code and governance for AI systems.