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Senior Research Associate, Virtual Cell Initiative - Single-Cell Screening

Futureshaper.com

Palo Alto (CA)

On-site

USD 80,000 - 120,000

Full time

6 days ago
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Job summary

A leading scientific institution is seeking a Senior Research Associate to join the innovative Genome Engineering Tech Center. This role focuses on developing a virtual model of the cell to identify therapeutic interventions for complex diseases, utilizing advanced CRISPR technologies. The successful candidate will work in a collaborative environment, addressing challenges through high-throughput experimentation and computational predictions.

Qualifications

  • Experience with cell culture and CRISPR screens is essential.
  • Strong cloning and subcloning skills required.
  • Excellent verbal and written communication skills needed.

Responsibilities

  • Contribute to single-cell CRISPR screening data generation.
  • Perform screens across diverse cell lines.
  • Collaborate on experimental workflows supporting the Virtual Cell Initiative.

Skills

Cell culture
CRISPR screens
Communication
Time management

Tools

FACS

Job description

About Arc Institute

The Arc Institute is a new scientific institution that conducts curiosity-driven basic science and technology development to understand and treat complex human diseases. Headquartered in Palo Alto, California, Arc is an independent research organization founded on the belief that many important research programs will be enabled by new institutional models. Arc operates in partnership with Stanford University, UCSF, and UC Berkeley.

While the prevailing university research model has yielded many tremendous successes, we believe in the importance of institutional experimentation as a way to make progress. These include:

  • Funding: Arc will fully fund Core Investigator’s (PI’s) research groups, liberating scientists from the typical constraints of project-based external grants.
  • Technology: Biomedical research has become increasingly dependent on complex tooling. Arc Technology Centers develop, optimize and deploy rapidly advancing experimental and computational technologies in collaboration with Core Investigators.
  • Support: Arc aims to provide first-class support—operationally, financially and scientifically—that will enable scientists to pursue long-term high risk, high reward research that can meaningfully advance progress in disease cures, including neurodegeneration, cancer, and immune dysfunction.
  • Culture: We believe that culture matters enormously in science and that excellence is difficult to sustain. We aim to create a culture that is focused on scientific curiosity, a deep commitment to truth, broad ambition, and selfless collaboration.

Arc has scaled to nearly 200 people. With $650M+ in committed funding and a state of the art new lab facility in Palo Alto, Arc will continue to grow quickly in the coming years.

About the position

We are seeking a Senior Research Associate experienced in cell culture and genetic screens to join Paul Datlinger's group within the Genome Engineering Tech Center at Arc. As a key member of Arc's Virtual Cell Initiative, you will work at the forefront of building a virtual model of the cell to identify therapeutic interventions for human diseases. You will join a team with a track record of foundational contributions in the field, including some of the first studies of mammalian CRISPR genome engineering (CRISPRi, a, ko, Cas13), single-cell functional genomics (CROP-seq), and DNA foundation modeling (Evo).

A successful candidate will have the opportunity to perform cancer cell line screens for single-cell perturbation datasets to support Arc's Virtual Cell Initiative. The candidate would be given the opportunity to optimize and perform screens across many cell lines, and the role will evolve over time to include screening support for more Virtual Cell Initiative goals. A candidate with excellent attention to detail and a methodical approach to experimental planning would flourish in this role as there are many different elements to combine across many experiments. This is a unique opportunity for a creative scientist to make a significant contribution at the intersection of biology and AI. If you’re ready to shape the foundation of the Virtual Cell Initiative, we invite you to apply for this exciting role.

About you

  • You are a creative scientist and are excited about the concept of iterative biology based on computational predictions from AI models.
  • You have an interest in single-cell RNA sequencing, and enjoy the challenge of high-thoughput experiments
  • You are willing to try new things as the project needs them and enjoy taking a creative approach to troubleshooting
  • You are excited to work in a multidisciplinary environment, and collaborate across mammalian models teams, stem cell platform teams, multiomics teams, NGS teams, and computational biology teams.

In this position you will

  • Contribute to the development of Arc’s Virtual Cell Initiative by generating single-cell CRISPR screening data that is scaled to millions of cells per experiment.
  • Perform single-cell CRISPR screens in diverse cell lines and in cell villages
  • Collaborate with teams across Arc to develop experimental workflows that support multiple aspects of the Virtual Cell Initiative, including model training and biological model development
  • Manage collaborations with internal groups and external stakeholders, such as CROs and biotech partners.

Requirements

  • Confidence in cell culture skills, including cell line engineering and lentivirus production
  • Experience with CRIPSR screens applied to in vitro cell culture
  • Strong cloning and subcloning skills
  • Excellent communication skills, including written and verbal
  • Strong time management skills and the ability to generate expected timelines for screening projects

Preferred Qualifications

  • Experience with single-cell CRISPR screens
  • Competent with FACS and flow cytometry for basic cell enrichment procedures or analysis
  • Ability to analyze NGS data for QC metrics
  • Experience cloning large libraries for sequencing experiments
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