Our mission is to make biology easier to engineer. Ginkgo is constructing, editing, and redesigning the living world to answer global challenges in health, energy, food, materials, and more. Ginkgo bioengineers make use of an in-house automated foundry for designing and building new organisms.
The Protein Engineering Team works to address the complex challenges of enzyme discovery, characterization, and engineering. We utilize state-of-the-art bioinformatics to discover novel enzymes and employ a growing suite of computational protein design tools for rationally designing improvements.
As a Computational Protein Engineer, you will contribute to the development of an in silico platform for rational design and apply state-of-art computational technologies to address the complex challenge of enzyme engineering. You will support Ginkgo’s ultra high-throughput protein engineering pipelines by building software to design and analyze large experimental datasets. In addition, you will have access to the Ginkgo platform and resulting large datasets for generating and testing hypotheses using deep learning approaches.
We are looking for someone who is excited about the promise of synthetic biology and the premier role of biomolecular engineering in biology by design. If you sleep not only to maintain healthy levels of protein in the brain, but also dream of harnessing the power of deep learning to design enzymes and other proteins, then you are at the right place.
Subject matter expertise: Track, technically analyze, and summarize new developments and emerging technologies in the field of machine learning, and provide recommendations on their application to protein sciences and synthetic biology.
Biomolecular modeling: Develop methods for applying machine learning to generate hypotheses and predictions about relationships between protein sequence, structure, dynamics, and function.
Biological data processing: Design, build, and train deep learning models for the analysis of large datasets to critique current hypotheses, spark new ones, and provide actionable information to aid in protein design tasks.
Model interpretation and application: Understand and predict the effects of sequence variation on protein function and biophysical parameters that are relevant for enzyme and protein engineering and improvement.
Protein engineering: Rational design of large libraries for high-throughput experimental characterization such as activity screens, multiplexed mutagenesis assays, and display technologies.
Technical consulting: Provide expertise on machine learning and protein engineering to support program and business development. Translate company and program goals into scientific and technical projects with clear objectives, measures of success in collaboration with other stakeholders throughout the company.
Interdisciplinary research: Flair for collaboration between scientists, who may speak somewhat different scientific languages, but all share a common passion for synthetic biology.
Experience and Capabilities
PhD in bioengineering, computer science, biophysics, biochemistry, physics, computational biology, bioinformatics, quantitative biology, or related field. Additional experience in postgraduate research or industry research is a plus.
Significant hands-on technical experience in applying state-of-art machine learning approaches to biological data analysis, especially within the domains of protein bioinformatics, structural biology, and biochemistry. Application of deep learning to protein engineering tasks is a plus.
Extensive knowledge of current neural network architectures such as transformers, convolutional neural networks, autoencoders, large language models, attention models, etc. Proficiency in supporting software libraries such as tensorflow, pytorch, keras, and scikit for model construction.
Experience parsing large datasets and applying machine learning to develop data-driven predictive models for interpretation of biological data and industrial bioengineering.
Fluency in at least one software programming language – Python strongly preferred. Familiarity of best practices for software development, including version control, code reviews, unit testing, and continuous integration. Experience in ML models management, MLOps, is a plus.
Familiarity with at least one type of molecular modeling software such as PyMOL, Rosetta, Schrodinger, Molecular Operating Environment (MOE). Expertise in the computational modeling of biomacromolecules is a plus.
Enthusiasm to learn new techniques. Strong curiosity of areas of biology previously unknown to you.
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We also feel that it’s important to point out the obvious here – there’s a serious lack of diversity in our industry, and that needs to change. Our goal is to help drive that change. Ginkgo is deeply committed to diversity, equity, and inclusion in all of its practices, especially when it comes to growing our team. Our culture promotes inclusion and embraces how rewarding it is to work with people from all walks of life.
We’re developing a powerful biological engineering platform, so we must remain mindful of the many ways our technology can – and will – impact people around the world. We care about how our platform is used, and having a diverse team to build it gives us the best chance that it’s something we’ll be proud of as it continues to grow. Therefore, it’s critical that we incorporate the diverse voices and visions of all those who play a role in the future of biology.
It is the policy of Ginkgo Bioworks to provide equal employment opportunities to all employees and employment applicants.