Zymergen is hiring a Bioinformatics Scientist for enzyme optimization in strain engineering programs, via iterative cycles of Design-Build-Test-Learn, each supported by software-aided design and analysis software. The Bioinformatics Scientist will focus on working with colleagues to develop strategies and data-driven approaches to sampling enzyme diversity from metagenomic sequences, as well as enzyme performance optimization via protein engineering. The Scientist will join a team of Bioinformatics and Data Scientists working on metabolic pathway and strain design, genetic construct design, phenotype and genome analysis. Our aim is to improve the performance of microbes via multiple, iterative, rounds of genome engineering. In addition, the Scientist will be part of a larger technical team to develop best practices in designing strain edits and computer-aided learning for strain performance improvement.
Explore state-of-the-art machine learning approaches to structure & function prediction from protein sequences, using our in-house LIMS datasets of sequences paired with enzyme performance data.
Develop enzyme sequence selection strategies to sample enzyme sequence diversity contained in our proprietary metagenomics libraries, with 250M+ metagenomic sequences
Evaluate available literature for a variety of metabolic enzymes in order to suggest strategies for metagenomic enzyme selection
Prepare and present organized work plans and work product to multiple levels of management and diverse audiences
Collaborate with academic and industrial business partners as needed to evaluate new technologies and/or to pursue joint ventures
- Master’s (or Bachelor’s with 2+ years ML experience) in one of the following relevant disciplines: Computer Science, Bioinformatics, Computational Biology, Biochemistry, Cell Biology,
- Metabolic Engineering
- Strong Python coding experience, including object-oriented programming
- Experience with biological sequence handling (DNA/RNA/protein)
- Experience with Tensorflow, PyTorch or related ML frameworks
- Experience with SQL DB access via Python
- Strong desire to collaborate and work with teams
- Strong interpersonal skills. As a team, we work from a base of trust and assumption of good will
- Ph.D. in a relevant discipline
- Rosetta, PyMol or related protein structural analysis
- Experience with protein engineering
- Experience in a metabolic engineering lab or company
- Experience with ‘omics analysis (proteomics, genomics, metabolomics, etc.)
- Experience with evolutionary and co-evolutionary analysis of protein sequences.
- Experience with deep neural networks for biological sequences (e.g. Transformers, LSTMs, CNNs)
You should be familiar with (at least through coursework) and ideally, have applied current approaches to protein sequence modeling and structure / function prediction. Examples of these include Transformers (e.g. ESM-1b or MSA Transformer), LSTMs (e.g. UniRep), generative adversarial networks (GANs), autoencoders, convolutional neural networks (CNNs), as well as other deep neural network (DNN) architectures.
You should have some experience in machine learning (ML) frameworks, including (but not limited to) PyTorch, Keras, Tensorflow, scikit-learn, caret (in R language), etc.
You will need sufficient Python software engineering experience, above the level of a typical bioinformatics scientist (i.e. at the level of a Software Engineer I-II). This candidate should have a good understanding of object oriented programming in Python, as well as the use of integrated development environments (IDE’s), Git/GitHub, Python package management systems like pip, pipenv, conda and poetry, etc.
Legal authorization to work in the U.S. is required. Zymergen may agree to sponsor an individual for an employment visa now or in the future if there is a shortage of individuals with particular skills for this job.
In compliance with federal law, all persons hired will be required to verify identity and eligibility to work in the United States and to complete the required employment eligibility verification form upon hire.