Are you a researcher passionate about artificial intelligence, biomedical research, computational biology, cancer research, or using machine learning to solve some of the world’s most complex scientific and medical challenges?

Google DeepMind is exploring opportunities for full-time Research Scientists working on AI for Science and Medicine, with a particular focus on biomedical AI, computational biology, and applications of artificial intelligence to cancer research.

This opportunity is particularly relevant to researchers who want to move beyond theoretical research and contribute to AI systems capable of accelerating scientific discovery and supporting advances in medicine.

The research opportunity is based in Mountain View, California, within the broader Google DeepMind research environment. Google DeepMind describes its Research Scientists as researchers who formulate novel hypotheses, develop new algorithmic approaches, design and evaluate models, conduct research projects, and contribute to foundational research.

Importantly, the opportunity information supplied for this announcement is an informal expression-of-interest form managed by the research team. It should therefore not be confused with a formal Google DeepMind employment application. The research team uses the form to explore potential candidates and mutual fit, while formal evaluation and recruitment take place through official recruiting channels.

About the Google DeepMind AI for Science and Medicine Opportunity

Artificial intelligence is increasingly being used to tackle complex problems in science and healthcare.

Rather than limiting AI to conventional applications such as search, recommendation systems, or language generation, researchers are developing increasingly sophisticated models capable of helping scientists understand biological systems, discover molecules, predict disease processes, analyze medical information, and accelerate scientific experimentation.

Google DeepMind has positioned scientific discovery as an important area of its work. Its careers information highlights examples of AI being used to help scientists understand molecular interactions, advance research into diseases, discover candidate materials, and improve scientific forecasting.

The Research Scientist opportunity described here is aimed at researchers interested in pushing this type of work further.

The focus areas identified in the opportunity include:

• AI for Science
• AI for Medicine
• Biomedical Artificial Intelligence
• Computational Biology
• Cancer Biology
• Machine Learning
• Scientific Discovery
• Medical Research

For researchers working across disciplines, this creates an opportunity to combine advanced AI methods with scientific and medical expertise.

What Makes This Research Opportunity Significant?

Cancer remains one of the world’s most challenging health problems, requiring advances across prevention, diagnosis, biological understanding, treatment development, and patient care.

AI can potentially contribute by helping researchers analyze complex biological data, identify patterns that are difficult to detect manually, model biological processes, generate scientific hypotheses, and accelerate the development of new approaches.

For a researcher, therefore, the attraction of this opportunity is not simply working for a globally recognized technology organization. It is the possibility of applying advanced computational methods to questions with significant scientific and medical consequences.

Google DeepMind’s careers information emphasizes its goal of developing breakthrough AI while using the technology to advance science and potentially improve lives globally.

Research Areas of Interest

The opportunity specifically highlights AI for Science and Medicine, Biomedical AI, and Computational Biology.

1. AI for Science

AI for Science involves applying artificial intelligence and machine learning to scientific questions.

Researchers in this area may develop models capable of:

• Identifying patterns in scientific datasets.
• Predicting scientific outcomes.
• Simulating complex systems.
• Generating scientific hypotheses.
• Automating aspects of scientific experimentation.
• Analyzing high-dimensional scientific data.
• Improving computational approaches to scientific discovery.

The field brings together machine learning, mathematics, statistics, computing, and domain-specific scientific knowledge.

2. AI for Medicine

AI for Medicine focuses on using artificial intelligence to address medical and healthcare problems.

Depending on the research project, this may include areas such as:

• Medical data analysis.
• Disease prediction.
• Clinical decision support.
• Biomedical modeling.
• Drug discovery.
• Medical imaging.
• Patient outcome prediction.
• Personalized medicine.
• Healthcare optimization.
• Modeling disease progression.

Researchers interested in this opportunity should be able to explain clearly how their work could contribute to AI-driven scientific or medical discovery.

3. Biomedical Artificial Intelligence

Biomedical AI sits at the intersection of machine learning, biology and medicine.

It can involve analyzing complex biological datasets and developing models that help researchers understand biological mechanisms and disease processes.

Relevant experience could include work involving:

• Genomics.
• Proteomics.
• Transcriptomics.
• Biomedical imaging.
• Clinical datasets.
• Biological sequence modeling.
• Drug discovery.
• Disease modeling.
• Multi-modal biomedical data.
• Computational approaches to cancer research.

4. Computational Biology

Computational biology combines computational methods with biological research.

Researchers may use machine learning, statistics, mathematical modeling, and computational techniques to investigate biological systems.

Candidates with experience applying AI or machine learning to biological problems may therefore find this opportunity particularly relevant.

5. Cancer Biology

Cancer research is specifically highlighted in the opportunity.

Researchers interested in cancer biology may be able to contribute through areas such as:

• Cancer genomics.
• Tumor biology.
• Disease progression.
• Drug response prediction.
• Cancer treatment modeling.
• Biomarker discovery.
• Computational oncology.
• AI-assisted drug discovery.
• Patient-level modeling.
• Biological data interpretation.

Candidates should use their application responses to clearly explain the cancer-related questions they are most interested in solving.

Key Details About the Opportunity

Position

Research Scientist: AI for Science and Medicine

Employment Type

Full-time

Level

The supplied opportunity identifies the role as L4.

Location

Mountain View, California, United States

Research Focus

The opportunity focuses on:

• AI for Science
• AI for Medicine
• Biomedical AI
• Computational Biology
• Cancer Biology
• AI-driven scientific discovery

Organization

Google DeepMind

Google DeepMind describes Research Scientists as creative and collaborative researchers who bring deep theoretical knowledge and interdisciplinary expertise to advance AI research. Research Scientists may formulate new hypotheses, develop algorithmic paradigms, conduct research projects, design and evaluate models, and contribute to research publications.

What Would a Research Scientist at Google DeepMind Do?

Although the supplied interest form does not provide a complete formal job description, Google DeepMind’s description of Research Scientist roles provides useful context.

Research Scientists are expected to work on difficult, open-ended research problems rather than simply implementing predetermined technical solutions.

Typical activities can include:

1. Identifying important research questions

   Researchers determine which scientific or technical problems are worth investigating and formulate hypotheses that can be tested experimentally.

2. Developing new AI approaches

   A Research Scientist may develop new models, algorithms, architectures, training approaches, or evaluation methods.

3. Conducting experiments

   Research ideas need to be tested rigorously. This can involve designing experiments, training models, evaluating performance, and interpreting results.

4. Working across disciplines

   AI for medicine and science requires collaboration between computer scientists, biologists, medical researchers, engineers, statisticians, and other specialists.

5. Publishing research

   Google DeepMind notes that Research Scientists frequently contribute to foundational research papers and participate in the wider research community.

6. Translating research into real-world impact

   The broader Google DeepMind research environment emphasizes moving promising ideas toward practical applications and scientific impact.

Who Should Consider Applying?

This opportunity could be particularly attractive to researchers who can demonstrate both strong research ability and a clear interest in applying AI to scientific or medical problems.

You may want to consider expressing your interest if you have experience in areas such as:

• Machine learning.
• Deep learning.
• Artificial intelligence.
• Computational biology.
• Bioinformatics.
• Biomedical engineering.
• Cancer research.
• Computational medicine.
• Systems biology.
• Genomics.
• Medical data science.
• Scientific computing.
• Mathematical modeling.
• AI-based drug discovery.
• Biomedical imaging.
• Statistical learning.
• Generative AI applied to science or medicine.

The strongest candidates are likely to be those who can clearly connect their previous research to the problems Google DeepMind is trying to solve.

Post-PhD and Industry Experience

One particularly important part of the supplied interest form asks candidates to indicate how many years of post-PhD research or industry experience they have.

The form provides the following options:

• None — just completed the PhD or about to complete it.
• Less than one year.
• 1–2 years.
• 3–5 years.
• 5+ years.

This suggests that the research team is interested in understanding candidates across different stages of the research career rather than relying solely on one fixed experience threshold.

Candidates should therefore accurately indicate their experience level rather than attempting to select a more senior category.

What Information Do Applicants Need to Provide?

The informal interest form asks candidates to provide several pieces of information that allow the research team to assess their background and research interests.

1. First and Last Name

Applicants must provide their full name.

2. Current Institution and Role

Candidates are asked to identify their current institution and position.

Examples could include:

• University Name — PhD Candidate
• Research Institute — Research Scientist
• University Name — Postdoctoral Researcher
• Biotechnology Company — Machine Learning Scientist

Applicants should provide their actual current role and institution.

3. CV or Resume

Candidates are required to upload a CV or resume.

The supplied form specifies:

• PDF format.
• Maximum file size of 10 MB.

Your CV should ideally make it easy for a research team to understand your:

• Academic background.
• Research experience.
• Technical expertise.
• Publications.
• Research projects.
• Awards and distinctions.
• Industry experience.
• Programming or machine learning skills.
• Biomedical or scientific expertise.
• Collaborative research experience.

For a Research Scientist opportunity, a generic CV may be less effective than a research-focused CV that clearly demonstrates your scientific contributions.

4. Google Scholar Profile

The form asks applicants to provide a link to their Google Scholar profile.

This allows the research team to quickly review the candidate’s publication record and research trajectory.

Before submitting, candidates should ensure their Google Scholar profile is accurate and up to date.

5. Personal Website or LinkedIn Profile

Applicants are also asked to provide a link to either their personal website or LinkedIn profile.

A personal research website can be particularly useful because it can provide additional information about:

• Research interests.
• Publications.
• Projects.
• Research collaborations.
• Academic achievements.
• Technical work.
• Talks and presentations.

6. Post-PhD or Industry Experience

Candidates must select the appropriate experience category from the options provided in the form.

7. Research Interests

Applicants must describe their research interests and identify specific areas within:

• AI for Science.
• AI for Medicine.
• Cancer Biology.
• Biomedical AI.
• Computational Biology.

This is likely one of the most important sections of the interest form because it gives candidates an opportunity to explain what problems they are passionate about solving.

8. Representative Research Paper

Candidates are asked to provide a link to one paper that best represents their research expertise.

They must also briefly explain why they selected that particular publication.

A strong response should not simply state that the paper is the candidate’s “best paper.”

Instead, candidates should explain what the publication demonstrates about their research ability, such as:

• A novel methodology.
• A significant scientific finding.
• A machine-learning innovation.
• A biomedical application.
• A difficult technical problem they solved.
• An interdisciplinary contribution.

9. Motivation for the Role

Applicants are asked why they are excited about the opportunity and what unique skills or perspectives they would bring to AI-driven scientific discovery.

This is an opportunity to connect personal research experience with Google DeepMind’s broader scientific mission.

How to Prepare a Strong Expression of Interest

Because this is an informal research-team interest form, applicants should approach it as more than an administrative questionnaire.

The form gives candidates a chance to present their research identity.

Step 1: Tailor Your CV

Do not submit a generic CV that could be used for any job.

Emphasize research that relates to:

• AI.
• Machine learning.
• Science.
• Medicine.
• Biology.
• Cancer.
• Computational research.

Where possible, quantify your contribution to research projects.

Step 2: Make Your Research Interests Specific

Avoid vague statements such as:

“I am interested in using AI to improve healthcare.”

Instead, identify the specific problem you want to solve.

For example, your response could explain your interest in developing machine-learning models for cancer progression, multimodal biomedical data, treatment response prediction, or computational approaches to biological discovery.

Step 3: Select Your Strongest Research Paper

Choose a publication that demonstrates your ability to conduct meaningful research.

The paper does not necessarily have to be your most recent publication. It should be the one that best demonstrates the expertise relevant to this opportunity.

Step 4: Strengthen Your Online Research Profile

Make sure your Google Scholar profile and personal website or LinkedIn profile are professionally presented.

Check for:

• Broken links.
• Incorrect publication information.
• Missing recent publications.
• Outdated job titles.
• Inconsistent academic information.

Step 5: Explain Your Unique Perspective

The form specifically asks what unique skills or perspectives you would bring.

Think beyond listing technical skills.

Consider whether your experience gives you a distinctive perspective because of:

• Interdisciplinary research.
• Experience working with clinical teams.
• Biomedical domain expertise.
• Machine-learning innovation.
• Scientific computing.
• Experience translating research int