Research Engineer, GenAI, Info Task, DeepMind
Posted
Summary
Research Engineer on Google DeepMind's GenAI information-task team: builds rigorous LLM evaluations, hillclimbs model quality, and develops modular routing/multi-agent solutions that integrate across Google products, collaborating with Eng, PM, and Data Science partners.
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.
We are a full-stack modeling team. In this role, you will develop rigorous evaluations to motivate directions for model improvements, hillclimb to improve the model itself, and collaborate with product teams to ensure an effective feedback loop as our solutions aim for efficiency at Google scale via techniques like routing, and are adaptable to standalone and multi-agent systems.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 varied learning opportunities and 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.
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.
We are a full-stack modeling team. In this role, you will develop rigorous evaluations to motivate directions for model improvements, hillclimb to improve the model itself, and collaborate with product teams to ensure an effective feedback loop as our solutions aim for efficiency at Google scale via techniques like routing, and are adaptable to standalone and multi-agent systems.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 varied learning opportunities and 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.
- Build rigorous evaluations to systematically measure model performance across various workloads and drive continuous quality hillclimbing.
- Develop modular, adaptable solutions that enable seamless integration across multiple Google product areas.
- Collaborate cross-functionally with Eng, PM, and Data Science partners to translate product requirements and feedback into model quality improvements.
Minimum qualifications:
- Bachelor's degree in Computer Science, Machine Learning, a related technical field, or equivalent practical experience.
- 3 years of experience with software development in Python or C++.
- Experience with ML frameworks (e.g., PyTorch, TensorFlow, JAX), crafting model evaluations and hillclimbing model performance.
Preferred qualifications:
- Advanced degree (Master's or PhD) in Machine Learning, Computer Science, or a related technical field.
- 2 years of experience designing benchmark suites and advancing model quality.
- Experience with Large Language Model (LLM) orchestration, routing, or multi-agent workflows.
- Strong cross-functional leadership and communication skills to effectively work across engineering, research, and product teams.