Research Engineer, Biosecurity, DeepMind

• 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.

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.

• 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).