The role
From Google's own posting.
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 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 .
Uncover opportunities in the intersection of Gemini, Search, factuality, continual learning, deep research, and more.
Analyze models and model-driven products beyond what the current leaderboards show and understand the problem space and create new ways to measure ideal behavior.
Use your findings to create practical interventions to improve our models across all downstream surfaces.
Collaborate with product teams (e.g., Search, YouTube) to scale research into production environments.
Minimum qualifications:
PHD in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or a related technical field, or equivalent practical experience.
5 years of experience in machine learning, with a focus on Large Language Models (LLMs) or Information Retrieval (IR).
3 years of experience with software engineering in one or more programming languages or frameworks (e.g., Python, C++, JAX, PyTorch), including experience implementing multi-stage training pipelines or algorithms.
Preferred qualifications:
Experience developing Reinforcement Learning (RL) algorithms, automated evaluation systems, or LLM reflection and reasoning frameworks.
Experience rapidly prototyping and iterating on complex systems, generative AI models, or multi-stage pipelines.
Experience collaborating with product engineering teams to deploy machine learning models or research innovations into large-scale production environments (e.g., Search, Recommendation Systems)
Track record of learning new software tools, frameworks, and codebases quickly to solve complex technical obstacles.
PHD in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or a related technical field, or equivalent practical experience.
5 years of experience in machine learning, with a focus on Large Language Models (LLMs) or Information Retrieval (IR).
3 years of experience with software engineering in one or more programming languages or frameworks (e.g., Python, C++, JAX, PyTorch), including experience implementing multi-stage training pipelines or algorithms.