Scientist, Functional Genomics, Virtual Cell Initiative

About Arc Institute Arc Institute is an independent nonprofit research organization at the interface of artificial intelligence and biology, working to accelerate scientific progress and understand the root causes of complex diseases. Founded in 2021 and based in Palo Alto, Arc partners with Stanford University, UC Berkeley, and UC San Francisco. Unlike academia, our scientists have long-term funding and industry-like resources. Unlike industry, they're free to pursue high-risk, long-term research without commercial pressures. Arc's Technology Centers and Core Investigator labs work side by side, integrating experimental and computational biology under one roof to tackle problems neither could solve alone. Our two Institute Initiatives reflect this model in action: Virtual Cell Initiative : Building a full-stack virtual cell model to identify disease mechanisms and nominate drug targets, accelerating the path from biological insight to clinical trials. Alzheimer's Disease Initiative : Mapping the genes, pathways, and environmental factors behind Alzheimer's disease to develop drug candidates that address root causes. More than 300 Arconauts work together at our Palo Alto headquarters, backed by substantial long-term philanthropic funding. About the position We are seeking a highly skilled and motivated Scientist with a strong background in functional genomics and single-cell sequencing to join the Virtual Cell Initiative at Arc Institute. The initiative aims to create the first foundational model that makes accurate predictions of cellular states and their response to intervention. In this role, you will lead the generation of massive single-cell perturbation datasets across 50+ cancer cell lines to train Virtual Cell models. You will be responsible for the end-to-end experimental workflow—from mammalian cell culture and large-scale CRISPR screening to single-cell library preparation and sequencing. Beyond technical execution, this role requires a scientist with exceptional project management capabilities, proficient in organizing complex data streams and adhering to timelines. The successful candidate will thrive within our vibrant team science culture, collaborating daily with other wet lab scientists, automation engineers, and computational biologists. We believe that the most ambitious science is achieved together, and in this highly integrated role, you will bridge the gap between experimental biology and computational innovation. About you You are passionate about the intersection of high-throughput biology and AI/ML to advance human health. You enjoy working collaboratively with others and thrive in a fast-paced, dynamic, team science environment. You are committed to scientific rigor, generating high-quality data, and maintaining thorough documentation practices. You possess a process-oriented mindset, constantly seeking ways to optimize workflows for efficiency and scalability. You are diligent, detail-oriented, and excel at troubleshooting complex experimental pipelines. You excel at organization, time management, and task prioritization, and enjoy independently driving multiple projects in parallel. In this position you will Execute large in vitro perturbation screens in diverse cell types to generate high quality single-cell perturbation datasets. Work with our interdisciplinary teams to further optimize functional genomics platforms and CRISPR technologies for large-scale data generation. Utilize project management systems to track progress and adhere to timelines, and document experimental data on ELN/LIMS in an organized manner. Interact cross-functionally with wet lab, computational, and machine learning teams to generate, analyze, and interpret data. Present key research findings at internal meetings and seminars, and external conferences. Requirements PhD in Cell Biology, Molecular Biology, Genomics, or another relevant field. 0-3 years of post-PhD experience in academia or industry. Extensive experience with large-scale pooled CRISPR screening, preferably with scRNA-seq readouts (e.g. Perturb-seq, CROP-seq). Expertise in mammalian cell culture and ability to manage multiple cell lines simultaneously. Experience in multi-color flow cytometry. Experience with mammalian cell line engineering using transfection, lentivirus production and transduction, and other common approaches. Experience with different molecular biology techniques (cloning, PCR, etc). Demonstrated ability to work both independently and in a highly collaborative multidisciplinary environment. Strong project management skills with the ability to independently plan, execute, and deliver results on time. Experience with using various project management systems, ELN and LIMS (e.g. Asana, Benchling). Excellent written and verbal communication skills. Preferred Qualifications Hands-on experience with downstream genomics protocols, especially scRNA-seq (preferably 10X Genomics workflows). Experience designing and cloning large pooled CRISPR sgRNA libraries. Experience optimizing different CRISPR-Cas9 effector systems, such as CRISPRi and CRISPRa, including vector design and delivery. Experience performing FACS-based cell sorting. Ability to analyze large datasets using Python, R, or similar tools. Familiarity with different NGS technologies (Illumina, Ultima). Experience using automation to scale, streamline, and optimize large-scale experiments. The base salary range for this position is $121,250 - $159,500. 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.

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