Senior Scientist, High-Throughput Functional Screening

Location: Watertown, MA
Employment Type: Full-time, on-site

About Digital Biology

Digital Biology is the data lab for AI protein drug engineering, building a measurement-driven discovery engine for next-generation biologics. We combine high-throughput experimental systems, advanced molecular technologies, automation, and computational modeling to generate biological data at unprecedented scale and resolution.

Our team works across protein engineering, functional screening, spatial biology, and AI-guided drug discovery. We partner with leading biotechnology and pharmaceutical organizations to build training and validation datasets that enable the design of better therapeutic proteins.

The Position

Digital Biology is seeking a Senior Scientist to contribute to our high-throughput drug screening platform for 3D human tissues. The platform measures how multi-targeting antibodies behave in 3D human tissue environments.We brings together protein engineering, library generation, organoid and 3D tissue model development, high-resolution spatial phenotyping, and systems integration to run screens at scale.

This is a hands-on wet lab role for a skilled optimizer of complex, interdisciplinary workflows. You will improve assay performance through deliberately designed iteration cycles. -  strategically defining key readouts, and carefully modulating parameters and variables, toward building robust protocols. You will work closely with protein engineers, automation engineers, and computational scientists to turn these protocols into reliable, scalable operations.

Responsibilities

• Develop, optimize, and validate 3D tissue and organoid screening assays through structured, data-driven iteration cycles
• Engineer and characterize immortalized and primary cell lines with advanced genetic tools, including CRISPR-based methods and histology
• Design and build constructs and libraries for antibody and cell engineering
• Own tissue handling end to end, including embedding, sectioning, histology, and staining
• Run high-resolution imaging and spatial phenotyping, and make sure image data is quality-controlled and ready for analysis
• Partner with automation engineers to scale workflows and improve throughput, reproducibility, cost, and turnaround time
• Keep documentation, sample traceability, and data and metadata structure rigorous for downstream analysis and model training
• Analyze data independently and present results, risks, and recommendations to the team and to partners

Required Qualifications

• 3+ years of industry experience in biotech or pharma, including hands-on assay development and optimization
• Ph.D., or equivalent experience, in cell biology, bioengineering, molecular biology, or a related field
• Strong experience with organoids and/or 3D tissue models
• Fluency in tissue handling, including sectioning, histology, and IHC/IF
• Fluency in fluorescence and high-resolution microscopy
• Deep experience in cell engineering, including CRISPR, and in mammalian expression vector design and molecular cloning
• A record of systematically optimizing complex, multi-step assays, with careful experimental design and troubleshooting

Preferred Qualifications

• Experience with spatial biology or multiplexed imaging platforms
• Experience with antibody engineering, pooled libraries, or high-throughput screening
• Familiarity with NGS workflows and data interpretation
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• Python or image-analysis tools (e.g., napari, CellProfiler, QuPath)

What We Offer

• The chance to shape the data foundations behind AI-driven protein and antibody design
• Building, scaling, and using one of the most powerful wet-lab screening platforms Close collaboration with experts in protein engineering, bioinformatics, automation, and computation
• An ambitious team solving hard problems at the intersection of hard tech, biology, and ML
• Competitive salary, equity, and benefits

Digital Biology is an equal-opportunity employer. We value different experiences, perspectives, and ways of thinking, and we believe the strongest scientific teams are built by bringing together people with diverse backgrounds and expertise.