Lead Research Engineer, Bioresilience
Confirmed on the employer's own hiring board on Oct 29, 2026. First seen by Alion on Oct 28, 2026.
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.
As a part of the Lead Research Engineer, you will join the Bioresilience Program (https://deepmind.google/blog/our-approach-to-bioresilience/) within DeepMind Impact Accelerator. You will lead development and deployment of applications leveraging frontier AI and generative models.
In this role, you will collaborate with Biosecurity, GenAI, and Alphabet partners like Google Research, Cloud, and Isomorphic Labs, build solutions for biological prevention, detection, and response. You will translate AI breakthroughs into practical tools for pathogen detection, crop resilience, and medical countermeasures.
Your role requires expertise in life sciences, computational biology, or biosecurity alongside strong software engineering leadership to build products with state-of-the-art GenAI.
Join Google DeepMind to advance AI development, strengthen global biological resilience, and safeguard public health.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 varied learning opportunities and career pathways for those driven to achieve exceptional results through collective effort.
Responsibilities
- Partner with GDI, research leadership, external authorities (biosecurity organizations, institutes, public health bodies), and cross-functional teams to navigate ambiguity, understand real-world bioresilience challenges, and set program objectives.
- Develop 0-to-1 solutions and workflows using internal platforms to build agents powered by foundation models such as Gemini, AlphaFold, and AlphaGenome, while curating specialized biological datasets, designing domain evaluations, and fine-tuning models to maximize real-world performance.
- Synthesize partner insights, edge cases, and deployment signals to inform future frontier model development and biological safety safeguards.
- Advocate best practices for safe GenAI deployment, demonstrating peak frontier model capabilities to solve high-impact biological problems end-to-end.
- Lead a team of software and research engineers, fostering an environment, developing talent, and partnering with leadership to prioritize high-impact opportunities.
Qualifications
Minimum qualifications:
- Bachelor’s degree or equivalent practical experience.
- 8 years of experience in software development.
- 5 years of experience in a technical leadership role; overseeing projects.
- 5 years of experience in a people management, supervision/team leadership role.
Preferred qualifications:
- 5 years of experience leading software teams or software projects.
- Experience applying deep learning models to biological data (such as functional genomics or protein sequence data).
- Experience manipulating common life science data types including FASTQ, SAM/BAM, gff, BED, VCF, and PDB/CIF using bioinformatics or health informatics tools.
- Ability to identify, prioritize, break down, and solve ambiguous and complex process and technical problems that span across many teams and organizations.
- Demonstrated domain knowledge in life sciences, computational biology, or biosecurity (e.g., molecular biology, pathogen genomics, computational plant pathology, protein engineering, bioinformatics, or metagenomics).
- Demonstrated ability to translate biological concepts into computational workflows and evaluation benchmarks.