Software Engineer, Bioresilience, DeepMind

• Bachelor's degree or equivalent practical experience.
• 8 years of experience in software development.

• 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.
• Ability to identify, break down, and solve ambiguous and complex process and technical problems that span across many teams and organizations.

Artificial intelligence will be one of humanity's most transformative inventions. At Deep Mind, 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.

• Partner closely with product leadership, external parties (e.g., biosecurity organizations, research institutes, public health authorities) and internal cross-functional teams to navigate ambiguity, deeply understand real-world biological defense challenges, and define clear product requirements and software architecture.
• Build robust data pipelines for complex biological datasets, implement scalable evaluation frameworks, and optimize model inference and serving infrastructure for production use.
• Contribute extensively to the engineering and development of novel solutions from 0 to 1, utilizing internal platforms and tools to build reliable production applications, agents, and workflows powered by GDM foundation models.
• Translate experimental research prototypes into maintainable software, sharing deployment telemetry, system performance constraints, and edge cases with core research teams to inform future model development.
• Set software engineering best practices for applied GenAI systems, focusing on testing, system reliability, security, and maintainability across deployed tools.

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