Both roles are highly collaborative, partnering closely with experimental scientists, bioinformatics infrastructure teams, machine learning researchers, and Arc investigators to identify biological mechanisms, nominate targets, and guide the design of future experiments.

About you

• You want to understand biological mechanisms and are not satisfied with lists of differentially expressed genes; you want to understand why perturbations produce specific cellular outcomes.
• You have deep hands-on experience with single-cell, perturbational, or multi-omic data and are comfortable working with large, messy biological datasets.
• You think carefully about experimental design and enjoy collaborating across disciplines: you can discuss gene regulation, cellular identity, and assay design with experimentalists, and data models, statistics, and software with computational colleagues.
• You have a solid understanding of existing computational methods and their limitations. When they are insufficient to answer the questions at hand, you can adapt and extend them.
• You are excited by the opportunity to work in a mission-driven, open-science research institute with close ties to Stanford, UCSF, and UC Berkeley.

In this position you will

• Conduct tertiary analyses of large-scale Perturb-seq, single-cell sequencing, multi-omic, and functional genomics datasets to identify functional relationships between genes, regulatory programs, and cellular phenotypes.
• Depending on your track, this includes work such as e.g. guide assignment, perturbation-effect estimation, and interaction modeling for pooled CRISPR screens, or trajectory and cell-state analysis of microglial state transitions in neurodegeneration.
• Partner with experimental teams on iterative study design, analysis, interpretation, and validation, discovering computational insights that translate into testable biological hypotheses.
• Work closely with bioinformatics and data infrastructure teams to define clean handoffs from primary and secondary analysis into exploratory and mechanistic modeling.
• Contribute to Arc's Virtual Cell and Alzheimer's Disease Initiatives by generating, analyzing, visualizing, and interpreting datasets that fuel predictive models.
• Develop reusable analysis notebooks, dashboards, software tools, benchmarks, and data resources that allow Arc scientists to explore complex datasets effectively.
• Present findings to internal stakeholders, and contribute to preprints or open-source projects when the opportunity arises or as needed.
• Mentor colleagues and interns and contribute to a collaborative, intellectually rigorous team environment.

Requirements

• PhD in computational biology, bioinformatics, genomics, systems biology, machine learning, computer science, molecular biology, or a related field, with 0–4 years of postdoctoral or professional research experience.
• Demonstrated track record of deriving biological insight from large-scale single-cell, perturbational, or multi-omic datasets.
• Hands-on experience with single-cell, perturbational, or multi-omic data, e.g. single-cell RNA-seq, Perturb-seq or CRISPR screens, single-cell ATAC-seq or multiome, including tertiary analysis, such as gene regulatory network inference, perturbation-effect or interaction modeling.
• Expertise in at least one of the following:
• Python proficiency, with experience using modern scientific computing and data analysis ecosystems.
• Familiarity with reproducible computational workflows, version control, and high-performance or cloud computing environments.
• Biological intuition and the ability to collaborate effectively with experimental scientists.
• Excellent written and verbal communication skills, with a track record of publications, preprints, open-source tools, or other scientific outputs.
• Ability to work in a fast-paced, ambitious, interdisciplinary research environment.
• Work a minimum of 3 days onsite in our Palo Alto office.

Preferred qualifications

• Background in cell identity, reprogramming, RNA biology, cell engineering, neurobiology, immunology, cancer biology, or complex disease genetics.
• Familiarity with dimensionality reduction and gene module analysis techniques in the context of single-cell biology.
• Experience in a startup, technology center, research institute, or other highly collaborative environment where scientific direction and technical execution are tightly coupled.

The base salary range for this position is $135,000 to $186,500. 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.

In addition to the Base Salary and Annual Bonus above, you will be eligible to participate in the Institute’s Compound Cash Grant Program (the “CCGP”). The CCGP is a program designed to offer multi-year cash grants providing for a fixed dollar amount paid out in semi-annual installments to reward employees for staying and contributing to the Institute. As soon as reasonably practical after your start date, you will receive a grant letter describing the terms of your award under the CCGP.