Software Engineer, Bioresilience, DeepMind
Summary
Builds 0-to-1 production GenAI systems on DeepMind's Bioresilience team in London: data pipelines for biological datasets, scalable evaluation frameworks, and model inference/serving infrastructure, translating research prototypes into reliable software. Requires 8 years of software development experience; deep learning on biological data is a plus.
- 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.
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience in software development.
Preferred qualifications:
- 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.






