Jobs

In this role, we are seeking a highly motivated and detail-oriented Senior Scientist to join our team at the frontier of spatial biology and AI, driving hands-on execution, protocol development, and quality standards for spatial biology experiments on human tissue samples in a lab built to fuel next-generation AI models. The ideal candidate will have deep hands-on experience with in situ hybridization & proteomics techniques, spatial data analysis, and demonstrate strong troubleshooting, mentorship, and organizational skills. This is an exciting opportunity to contribute to a multidisciplinary laboratory environment at the interface between Spatial Biology and AI model generation.

As we scale up our data generation engine, this role is focused on driving impact through day-to-day execution, troubleshooting, and quality enforcement across a high volume of runs.

KEY ROLE AND RESPONSIBILITIES

• Independently plan, perform and troubleshoot spatial biology experiments on human tissue samples.
• Establish and enforce data quality standards across the team's experiments and analysis pipelines.
• Mentor and train team members on technique, rigor, and quality judgment.
• Collaborate closely with cross-functional teams including Histology, and Scientific Operations to ensure seamless project execution.
• Actively participate in lab team meetings, fostering a collaborative and innovation-driven culture.
• Lead cross-functional projects with Engineering and ML Team members to design the next generation of data for AI model training.

REQUIRED EDUCATION & EXPERIENCE

• PhD in molecular biology, genomics, cell biology, biomedical engineering, or a related field.
• 2-5+ years of industry hands-on experience with spatial transcriptomics (CosMx, Xenium, MERFISH…); direct CosMx experience strongly preferred.
• Experience with spatial proteomics platforms (e.g., CosMx Protein, Akoya PhenoCycler, Lunaphore..) and immunofluorescence panel design are highly desirable.
• Proven ability to independently troubleshoot wet-lab and imaging-based assay failures, tracing issues back to root cause.
• Solid knowledge of spatial transcriptomics data QC metrics: transcript count distributions, segmentation quality, dropout/housekeeping-gene diagnostics, batch effects, and spatial artifacts.
• Comfortable enforcing standards across a team that includes both junior and more senior colleagues — this role requires leadership as well as technical skill.
• Excellent organizational, communication (oral and written), and documentation skills.
• Computational fluency.

Nice to have:

• Familiarity with cancer tissue biology and identity marker panels.
• Proficiency with Python or R for data analysis.
• Prior experience mentoring or informally leading junior lab staff.