Research Engineer, Biosecurity, DeepMind

Minimum qualifications:

• Bachelor’s degree in Computer Science, Machine Learning, Computational Biology, or a related field, or equivalent practical experience.
• 2 years of experience with machine learning algorithms and tools (e.g., TensorFlow, Pytorch).
  

Preferred qualifications:

• PhD degree in Computer Science, Machine Learning, Computational Biology, or a related field, or equivalent practical experience.
• Experience applying deep learning models to biological data (such as functional genomics or protein sequence data).
• Experience working with life science data, such as in bioinformatics or health informatics.
• Familiarity with emerging topics in biosecurity, such as metagenomic surveillance, de novo pathogen design, and LLM-based uplift.
• Ability to identify, breakdown, and solve ambiguous and complex process and technical problems that span across many teams and organizations.

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.

The GDM Biosecurity Initiative aims to leverage AI to proactively mitigate risks from biological threats, including those from nature or from intentional misuse of AI-enabled tools. The team conducts foundational biosecurity research across a range of topics, assists in biosecurity risk assessments of specialized science models, and engages with the broader AI biosecurity ecosystem.

As a research engineer on the biosecurity initiative, you will combine frontier ML research with software engineering best practices to build tools, models, and evaluation methodologies to mitigate biological risks. You will collaborate closely with research scientists and engineers to shape the research roadmap for the biosecurity initiative, specifically influencing data curation, scaling, infrastructure, and model development to accelerate efforts within the biosecurity portfolio aligned as required to support the initiative in collaboration with the team.

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.

Responsibilities

• Design and implement novel algorithms and machine-learning (ML) methods to address core biosecurity research questions.
• Contribute to pioneering research on AI and biosecurity, supporting scientific publications and product development aligned with the Biosecurity initiative mission.
• Collaborate with research scientists and engineers across the initiative and Science and Strategic Initiatives unit to prototype, experiment, and scale AI research using software engineering best practices.
• Contribute to the design of biological datasets, evaluation methodologies, and infrastructure required to pursue novel research hypotheses.
• Synthesize complex research findings into clear, actionable reports and presentations for both technical and non-technical stakeholders, delivered through written documentation and verbal briefings.