Research Engineer, Conversational Agentic AI, DeepMind

Socket.dev; Mountain View (CA); On-site; USD 207,000 - 300,000; Full time

Job summary

Google DeepMind is seeking a research-focused Software Engineer to advance AI systems, including multimodal agents, across global projects. You will prototype, test, and deploy ideas in real-world settings while staying connected to research communities.

Responsibilities include building scalable AI features, collaborating with cross-functional teams, and contributing to safety and ethics in AI development. The role emphasizes rapid experimentation and impact at scale.

Qualifications

• Bachelor’s degree in CS, ML, Math, Cognitive Science, or related 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 (LLMs, NLP, or data pipelines).
• Experience in ML, AI, AI algorithms, and data analysis.

Responsibilities

• Partner with Gemini or DeepMind teams to design, develop, and deploy novel multimodal conversational agents.
• Develop audio-first models capable of handling complex dialogs and external tools.
• Leverage new data sources to enable real-time dialog capabilities.
• Collaborate with infra teams to design models for streaming, low-latency dialog.
• Prototype and evaluate new technologies.

Skills

Data preparation

ML model training

AI features

Data analysis

Education

Bachelor's degree or equivalent

Job description

Minimum 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.

Preferred qualifications:

• One or more publications in conferences or journals (e.g., NeurIPS, ICML, ICLR, AAAI, CVPR).
• Research background in NLP/Generative AI.

About the job:

At Google, research-focused Software Engineers are embedded throughout the company, allowing them to setup large-scale tests and deploy promising ideas quickly and broadly. Ideas may come from internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world.

From creating experiments and prototyping implementations to designing new architectures, engineers work on real-world problems including artificial intelligence, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more. But you stay connected to your research roots as an active contributor to the wider research community by partnering with universities and publishing papers.

Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority.

We are pushing the boundaries across multiple domains. Our global teams offer learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.

Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities:

• Partner with the Gemini or DeepMind teams to design, develop, and deploy novel multimodal conversational agents.
• Develop audio-first models capable of orchestrating and planning complex dialogs, including leveraging external tools like search when necessary.
• Leverage new sources of data (real and synthetic) to empower new real-time dialog capabilities.
• Work with infra teams to design models suitable for streaming bi-directional dialog, so the user experience is always fluid and low-latency.
• Prototype and evaluate new technologies.