Senior Research Associate, Single Cell Screening

About Arc Institute

The Arc Institute is a new scientific institution conducting 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 fully funds Core Investigators’ (PIs’) 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 over 350 people to date. 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 highly skilled and motivated Senior Research Associate with a strong background in single-cell RNA-seq to join the Virtual Cell Initiative at Arc Institute. In this role, you will contribute to the generation of large-scale single-cell perturbation datasets across 50+ cancer cell lines to train Virtual Cell models to make accurate predictions of cellular state and their response to intervention.

You will use your experience implementing single-cell genomics technologies to scale our workflows for generating high-quality scRNA-seq datasets and contribute to critical data generation for Arc’s Virtual Cell Initiative. As part of a highly collaborative team, you will have opportunities to build expertise in cell line engineering, mammalian cell culture, and CRISPR screening. This role is ideal for someone who is detail-oriented and motivated by optimizing experimental workflows. The successful candidate will have excellent time management and communication skills and will thrive within our vibrant and collaborative culture.

About you

• You are passionate about working at the intersection of high-throughput biology and AI/ML to advance human health.

• You are process-oriented and are motivated by optimizing workflows for efficiency and scalability.

• You thrive in a timeline-driven, dynamic, team science environment.

• You hold yourself to a high standard of rigor and consistency in your experimental work.

• You are detail-oriented and maintain thorough documentation practices.

In this position you will

• Execute single cell genomics workflows, including sample processing, NGS library preparation, and QC.

• Support Arc’s Virtual Cell Initiative CRISPR screening efforts by working collaboratively to generate large in vitro gene perturbation datasets across many cell contexts.

• Optimize and troubleshoot our single cell sequencing and CRISPR screening workflows to maximize data quality, diversity, and scalability.

• Streamline high throughput experiments by implementing automation solutions.

• Utilize project management systems to track progress and ELN/LIMS systems to document experimental data.

• Communicate experimental progress and results at group and cross-functional meetings.

Requirements

• BS + 4 years or MS + 2 years of professional experience in cellular biology, molecular biology, biochemistry, neuroscience, or another relevant field.

• Expertise in running single cell RNA-seq workflows, including cell sample preparation, single cell capture, and library preparation. Experience with 10X Genomics technology is a plus.

• Hands-on experience with common molecular biology techniques, including PCR/qPCR/ddPCR, DNA and RNA purification, and nucleic acid QC and quantification (e.g., TapeStation, Qubit).

• Proficiency in aseptic technique for culturing mammalian cell lines.

• Strong attention to detail and ability to follow experimental protocols precisely.

• Experience using an ELN to document your work and track biological material. Familiarity with Benchling is a plus.

• Excellent written and verbal communication skills and a collaborative mindset.

Preferred Qualifications

• Experience working in an industry setting is desirable but not required.

• Experience implementing liquid handling solutions or automation for molecular biology workflows.

• Familiarity with performing large-scale pooled CRISPR screens.

• Proficiency in flow cytometry or cell sorting for single cell sample preparation.

• Experience with software for data analysis and visualization. Familiarity with analyzing data using Python, R, or another high-level programming language is a plus.

The base salary range for this position is $87,000 - $114,250. These amounts reflect the range of base salary that the Institute reasonably would expect to pay a new hire or internal candidate for this position. The actual base compensation paid to any individual for this position may vary depending on factors such as experience, market conditions, education/training, skill level, and whether the compensation is internally equitable, and does not include bonuses, commissions, differential pay, other forms of compensation, or benefits. This position is also eligible to receive an annual discretionary bonus, with the amount dependent on individual and institute performance factors.