Research Associate II

About the role

We are seeking a highly motivated Research Associate to join our growing translational laboratory team. This individual will be instrumental in developing, optimizing, and executing ex-vivo assays to support our oncology clinical pipeline. A primary responsibility will be to understand drug mechanisms of action in primary tumor samples and to validate AI-identified biomarkers to support patient stratification. This position requires strong technical skills, meticulous attention to detail, and a passion for data-driven discovery.

What You’ll Do

• Design and executing medium-to-high-throughput drug screen assays for testing tumor kill and pharmacodynamic effects using Incucyte.
• Characterization of assay samples for proteomics, RNA, DNA and epigenetic changes that explain molecular mechanisms of resistance and tolerability.
• Collaboration with a world-class computational team to interpret multi-omic data to improve AI predictions and drive biomarker strategy for clinical trials.
• Building and maintaining a biobank of primary tumor tissues and cell lines for translational experiments.
• Perform routine and advanced cellular and molecular biology techniques, including primary tumor sample processing, cell culture, transfection, nucleic acid extraction, PCR/qPCR, Western blotting, and immunostaining.
• Inventory management, vendor coordination, equipment maintenance scheduling, and compliance with safety protocols.
• Sample tracking, experiment recording and data management using LIMS system (Benchling).
• Contribution of biological insights to the preparation of reports, presentations, and publications for clinical trials and FDA documents.

Who You Are

• B.S. or M.S. in Biology, Molecular Biology, Genomics, or a related field.
• 3–5 years of hands-on experience in an oncology research or industry laboratory setting.
• Strong expertise in cell culture techniques, including the maintenance of human cancer cell lines, dissociation and expansion of primary solid tumor tissues from surgical resection. Experience with 3D culture systems such as Matrigel is preferred.
• Competency in setting up drug functional assays for small molecules and antibody therapeutics required.
• Familiarity with flow cytometry and/or immunohistochemistry in FFPE is highly desirable.
• Proficiency in genomic assays (e.g. RNAseq, DNAseq and ChIP) and CRISPR technology is also preferred.

Location

This position is fully on-site in our Manhattan laboratory and offers opportunities for professional growth in a collaborative research environment.

Pathos is building a next-generation biotech with AI at the core. Not as a feature, but as the operating system for how medicines get developed. We believe most drugs don't fail because the science was wrong. They fail because they were tested in the wrong patients, with the wrong assumptions, in trials that couldn't answer the real question: who benefits, and why?

Pathos exists to change that. We're building the largest foundation model in oncology and pairing it with proprietary AI systems, deep oncology expertise, and 200+ petabytes of multimodal data linked to patient outcomes, so we can make development decisions with more precision, much earlier.

This is not theoretical. We're well-capitalized and have the leadership to build a generational company. We invest in and advance our own clinical-stage programs, using our AI platform to sharpen trial design, patient selection and biomarker strategy. So therapies reach the patients most likely to benefit, sooner.

How We Build

Pathos does not operate like a traditional biotech. There is no middle management. There are no layers of approval. The company is designed, from the ground up, around small teams of 2–4 subject-matter experts who each command hundreds of AI agents to do the work that used to require dozens of people.

Everyone builds. Everyone ships. Every function at Pathos — from clinical execution to asset selection to the foundation model itself — runs on this model. Our product velocity delivers meaningful outcomes in hours instead of weeks. This is not a future aspiration. It is how we operate today.

The people who thrive here are operators: deep experts who can specify what needs to happen, orchestrate AI agents to execute at scale, and make high-judgment calls that compound over time. If you have spent your career building and shipping AI systems at scale, this is the environment where that experience becomes a superpower.