Research Scientist, Aqua, Deepmind

Google Google · Big Tech · Bengaluru, Karnataka, India

Research Scientist at DeepMind India focused on developing foundational capabilities in Large Models for Artificial General Intelligence (AGI) by advancing autonomous agents through reinforcement learning and ML optimization methods. The role involves designing, implementing, and evaluating models and agents, pushing the boundaries of RL, and collaborating with Responsible AI teams. Requires a PhD, experience with ML frameworks, deep learning, RL, and a publication record.

What you'd actually do

  1. Design, implement, and evaluate models, agents, and software prototypes of large foundational models.
  2. Push the boundary of Reinforcement Learning and ML optimization methods to build autonomous agents.
  3. Report and present research findings and developments including status and results clearly and efficiently both internally and externally, verbally and in writing.
  4. Suggest and engage in team and external collaborations, maintaining relationships with relevant research labs.
  5. Work in collaboration with our Responsible AI teams to ensure our advances in intelligence are developed ethically and provide broad benefits to humanity.

Skills

Required

  • PhD degree in Computer Science, Artificial Intelligence, or a related field, or equivalent practical experience.
  • 2 years of experience with machine learning frameworks (e.g., JAX, TensorFlow, or PyTorch).
  • Experience with advanced deep learning and reinforcement learning.
  • Publication record in machine learning conferences or journals (e.g., NeurIPS, ICML, ICLR, KDD, AAAI).

Nice to have

  • Experience with multimodal learning, large language models, or assistive AI agents.
  • Experience with prompt engineering, few-shot learning, post-training techniques, and evaluations.
  • Familiarity with large-scale model training and deployment.
  • Strong programming skills in Python or similar languages.
  • Excellent communication and collaboration skills.

What the JD emphasized

  • publication record in machine learning conferences or journals (e.g., NeurIPS, ICML, ICLR, KDD, AAAI)

Other signals

  • developing foundational capabilities in Large Models towards realizing Artificial General Intelligence (AGI)
  • uncovering emergent agentic behaviors through novel reinforcement learning algorithms run at scale
  • advancing in autonomous agents through reinforcement learning and ML optimization methods
  • developing novel algorithmic architecture towards the end goal of solving and building Artificial General Intelligence