Research Scientist, Google Ads
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What you'll do
- Author research papers to share and generate impact of research results across the team and in the research community.
- Help in growing research business across teams by sharing research trends and best practices within the community.
- Develop novel machine learning and large language modeling techniques that influence Google products.
- Post-train and finetune Large Language Models (LLMs) for large-scale retrieval, pseudo-rater, and other Google Ads applications.
- Collaborate closely with researchers across Google and DeepMind.
What they're looking for
- PhD in Computer Science, a related field, or equivalent practical experience.
- Experience working on modern large language model post-training (e.g., supervised fine-tuning, RLHF) or core generative model development in an industry AI lab, research institute, or frontier AI organization.
- One of more scientific publication submission(s) for conferences, journals, or public repositories (such as CVPR, ICCV, NeurIPS, ICML, ICLR, etc.).
Nice to have
- 3 years of experience working in frontier AI research labs on pre-training or post-training teams.
- 1 year of experience owning and initiating research agendas.
- 1 year of experience with generative AI concepts (e.g., LLMs, diffusion) and development workflows.
- Experience in large language models or other generative AI tuning and optimization techniques.
- First author publications in top machine learning conferences (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, COLM).
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
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.
Google Ads is helping power the open internet with the best technology that connects and creates value for people, publishers, advertisers, and Google. We’re made up of multiple teams, building Google’s Advertising products including search, display, shopping, travel and video advertising, as well as analytics. Our teams create trusted experiences between people and businesses with useful ads. We help grow businesses of all sizes from small businesses, to large brands, to YouTube creators, with effective advertiser tools that deliver measurable results. We also enable Google to engage with customers at scale.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.US: $147000 - $210000 (USD) + 15% bonus target + equity + benefits
Learn more about benefits at Google.
Responsibilities:
- Author research papers to share and generate impact of research results across the team and in the research community.
- Help in growing research business across teams by sharing research trends and best practices within the community.
- Develop novel machine learning and large language modeling techniques that influence Google products.
- Post-train and finetune Large Language Models (LLMs) for large-scale retrieval, pseudo-rater, and other Google Ads applications.
- Collaborate closely with researchers across Google and DeepMind.
Minimum qualifications:
- PhD in Computer Science, a related field, or equivalent practical experience.
- Experience working on modern large language model post-training (e.g., supervised fine-tuning, RLHF) or core generative model development in an industry AI lab, research institute, or frontier AI organization.
- One of more scientific publication submission(s) for conferences, journals, or public repositories (such as CVPR, ICCV, NeurIPS, ICML, ICLR, etc.).
Preferred qualifications:
- 3 years of experience working in frontier AI research labs on pre-training or post-training teams.
- 1 year of experience owning and initiating research agendas.
- 1 year of experience with generative AI concepts (e.g., LLMs, diffusion) and development workflows.
- Experience in large language models or other generative AI tuning and optimization techniques.
- First author publications in top machine learning conferences (e.g., NeurIPS, ICML, ICLR, ACL, EMNLP, COLM).
Company
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