Job description

As an organization, Google maintains a portfolio of research projects driven by fundamental research, new product innovation, product contribution and infrastructure goals, while providing individuals and teams the freedom to emphasize specific types of work. As a Research Scientist, you'll setup large-scale tests and deploy promising ideas quickly and broadly, managing deadlines and deliverables while applying the latest theories to develop new and improved products, processes, or technologies. From creating experiments and prototyping implementations to designing new architectures, our research scientists work on real-world problems that span the breadth of computer science, such as machine (and deep) learning, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more. As a Research Scientist, you'll also actively contribute to the wider research community by sharing and publishing your findings, with ideas inspired by internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world. 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 diverse 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: $174000 - $252000 (USD) + 15% bonus target + equity + benefits Learn more about benefits at Google . • Deliver step-change improvements to Gemini through rigorous and ambitious research. • Improve the models ability to manage its own context and memory natively, instead of relying on the code wrapped around the model (the harness). • Make Gemini better at subagent orchestration, which is basically memory management through isolation. • Advance complex long horizon task execution such as for RSI and computer use where we combine low latency asynchronous execution and long horizon reasoning. • Build novel infrastructure, gaining a deep understanding of training dynamics, and developing novel RL algorithms. • 2 years of experience with machine learning algorithms and tools, or applied ML (e.g., LLM's, generative AI, NLP, recommendations). • 2 years of experience with Generative AI, AI algorithms, and system design. • 2 years of industry experience in applied machine learning/machine learning research. • 2 years of experience with developing machine learning infrastructure, distributed systems or networks. • 2 years of experience in an applied research setting. • Experience coding in Python or C++. Minimum qualifications: • 2 years of experience with machine learning algorithms and tools, or applied ML (e.g., LLM's, generative AI, NLP, recommendations). • 2 years of experience with Generative AI, AI algorithms, and system design. • 2 years of industry experience in applied machine learning/machine learning research. • 2 years of experience with developing machine learning infrastructure, distributed systems or networks. • 2 years of experience in an applied research setting. • Experience coding in Python or C++. Preferred qualifications: • Master's degree or PhD in Computer Engineering/Machine Learning or equivalent. • Software engineering skills in Python and deep experience with modern ML frameworks (e.g., JAX, PyTorch).

Requirements

Must-haves

We are pushing the boundaries across multiple domains. Our global teams offer diverse 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.
Learn more about benefits at Google .
Deliver step-change improvements to Gemini through rigorous and ambitious research.
Improve the models ability to manage its own context and memory natively, instead of relying on the code wrapped around the model (the harness).
Make Gemini better at subagent orchestration, which is basically memory management through isolation.
Advance complex long horizon task execution such as for RSI and computer use where we combine low latency asynchronous execution and long horizon reasoning.
Build novel infrastructure, gaining a deep understanding of training dynamics, and developing novel RL algorithms.
2 years of experience with machine learning algorithms and tools, or applied ML (e.g., LLM's, generative AI, NLP, recommendations).
2 years of experience with Generative AI, AI algorithms, and system design.
2 years of industry experience in applied machine learning/machine learning research.
2 years of experience with developing machine learning infrastructure, distributed systems or networks.
2 years of experience in an applied research setting.
Experience coding in Python or C++.
2 years of experience with machine learning algorithms and tools, or applied ML (e.g., LLM's, generative AI, NLP, recommendations).
2 years of experience with Generative AI, AI algorithms, and system design.
2 years of industry experience in applied machine learning/machine learning research.
2 years of experience with developing machine learning infrastructure, distributed systems or networks.
2 years of experience in an applied research setting.
Experience coding in Python or C++.

Nice-to-haves

US: $174000 - $252000 (USD) + 15% bonus target + equity + benefits
Master's degree or PhD in Computer Engineering/Machine Learning or equivalent.
Software engineering skills in Python and deep experience with modern ML frameworks (e.g., JAX, PyTorch).

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aimachine learningmlllmpython

Platform: google
Effort: medium