Boulder, CO
Full Time

Research Associate

At Arpeggio we are focused on the discovery of therapeutics designed to treat transcriptionally-driven diseases. To do so, we have built a high-throughput RNA screening platform that identifies small molecules that distinctly impact diseased transcriptomes and ultimately disease progression. Our technology is based on new developments in RNA sequencing coupled to powerful machine learning to give an unprecedented look at how small molecules affect biological systems and where a therapeutic vulnerability may exist. We are experts in genomics, machine learning, and drug discovery, with the vision to transform the way we approach drug discovery and development.

Job Responsibilities

Our drug discovery group is seeking to hire a Research Associate to assist in the discovery of small molecules that target cancer cells by manipulating gene expression. Our laboratory uses discovery chemistry, functional genetic and chemical biology screens, and advanced transcriptional genomics approaches to discover and study drug-like small molecules that modulate disease-specific transcription. This Research Associate will assist with projects investigating the interplay between disease states, therapeutics, and transcription. Because Arpeggio strives to create a highly collaborative research environment, experience working well within teams is strongly preferred.

Job Requirements

- B.S./B.A. in a biology-related major

- 2-4 years of experience in an undergraduate research lab (thesis preferred) or

- 1-3 years of experience in the biotechnology/pharmaceutical industry

- Excellent organizational skills and attention-to-detail

- Mammalian cell culture; expanding and treating cell lines

- Quantitative molecular biology techniques such as western blotting, RT-qPCR, and high-throughput sequencing

- Experience with Dotmatics, CDD Vault, or similar chemical and biological registration ELN, inventory, and data visualization software

Preferred

- Experience with in vitro assay development (cell-based, biochemical and/or biophysical) high throughput screening and functional genomic approaches e.g., CRISPR-Cas9

- Experience with in vitro and in vivo oncology model systems

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