Join DeepMind India's Agents team, dedicated to advancing foundational capabilities in Large Models to achieve Artificial General Intelligence (AGI). Our focus lies in exploring emergent agentic behaviors through innovative reinforcement learning algorithms deployed at scale. We contribute research breakthroughs to Gemini and critical Google product launches, impacting millions of users. We are actively seeking a motivated Research Scientist in Bengaluru to enhance autonomous agents using reinforcement learning and ML optimization techniques.

This role involves groundbreaking research into Large Language Models (LLMs) with an emphasis on developing more capable autonomous agents. You will spearhead the creation of novel algorithmic architectures aimed at solving and building Artificial General Intelligence. Collaboration within and across research fields will be key as you tackle fundamental questions in machine learning and AI.

At Google DeepMind, we are at the forefront of AI innovation, leveraging interdisciplinary teams to address complex global challenges and accelerate high-quality product development for billions. Our technologies are applied for widespread public benefit and scientific discovery, with safety and ethics as our paramount concerns.

Design, implement, and rigorously evaluate models, agents, and prototype software for large foundational models.

Drive advancements in reinforcement learning and ML optimization methodologies to construct sophisticated autonomous agents.

Effectively report and present research findings, including project status and outcomes, both internally and externally, through clear verbal and written communication.

Propose and participate in collaborative efforts within the team and with external research laboratories, fostering and maintaining key relationships.

Partner with our Responsible AI teams to ensure ethical development of AI advancements that deliver broad benefits to humanity.

A PhD in Computer Science, Artificial Intelligence, or a related discipline, or equivalent practical experience is required.

Possess at least 8 years of hands-on experience with machine learning frameworks such as JAX, TensorFlow, or PyTorch.

Demonstrate a strong background in advanced deep learning and reinforcement learning techniques.

Showcase a notable publication record in prestigious machine learning conferences or journals (e.g., NeurIPS, ICML, ICLR, KDD, AAAI).

Preferred qualifications include experience with multimodal learning, large language models, or assistive AI agents, along with expertise in prompt engineering, few-shot learning, post-training techniques, and evaluations. Familiarity with model training and deployment is beneficial, alongside excellent programming skills in Python or similar languages. Strong communication and collaboration skills are essential.