Research Engineer, Gemini Retrieval, DeepMind

About the Job

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.

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.

Responsibilities

• Uncover strategic opportunities at the intersection of Gemini, Search, factuality, continual learning, deep research, and domain internalization.
• Analyze models and model-driven products beyond current leaderboards—understand complex problem spaces and create new ways to measure ideal behavior.
• Use empirical findings to develop practical interventions and modeling innovations to improve our models across downstream surfaces.
• Collaborate with product teams like Search and YouTube to scale research innovations into production environments, optimizing for quality and efficiency.

Qualifications

Minimum qualifications:

• Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or a related technical field, or equivalent practical experience.
• 3 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 Python, C++, JAX, or PyTorch programming, 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 with competitive programming, mathematics competitions (e.g., ICPC, IOI, IMO), or a track record of developing novel algorithms.
• 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.

Additional Information

Applicants in San Francisco: Qualified applications with arrest or conviction records will be considered for employment in accordance with the San Francisco Fair Chance Ordinance for Employers and the California Fair Chance Act.