Google DeepMind is looking to build the future of AI assistants by tackling foundational challenges in agentic reasoning, planning, and tool use, with a clear path to delivering these advancements to billions of users.
Requirements
- 3+ years of professional or academic experience in machine learning or a related field.
- Strong programming skills and experience in Python.
- Experience with one or more deep learning frameworks (e.g., JAX, TensorFlow, PyTorch).
- Experience in one of the following research areas: Reinforcement Learning, Large Language Models (LLMs), Agent-based Systems, or Natural Language Processing.
- Demonstrated experience building and training agentic AI systems, particularly with tool use (e.g., ReAct-style models).
- Deep expertise in Reinforcement Learning, especially in the context of large-scale models and long-horizon tasks.
- Experience with long-context models, context compression techniques, or state-space models.
Responsibilities
- Design, implement, and iterate on novel models and algorithms (e.g., Agentic RL) to improve agentic tool use.
- Tackle the core research problem on long-context reasoning.
- Develop sophisticated agentic orchestration from data, model training to inference.
- Explore and build advanced multi-agent systems in both Python for rapid experimentation and C++ for high-performance deployment.
- Continuously evaluate and benchmark the models against industry standards.
- Partner with product and engineering teams to integrate your research breakthroughs into live experimental product surfaces, directly influencing the future of Google's assistants.
Other
- Bachelor's or Master's degree in Computer Science, a related technical field, or equivalent practical experience.
- A PhD in Computer Science, Machine Learning, or a related field, with a strong publication record in top-tier conferences.
- Proficiency in Python/C++ for building high-performance, low-latency machine learning systems.
