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Transcripta Bio

Computational Biologist

Palo Alto, CA · On-site

... structural edge over conventional approaches. We are looking for a Senior Scientist to become a ... PhD in Bioinformatics, Computational Biology, Genomics, or a related field with 3+ years of ...

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How much do computational structural biology jobs pay per hour?

As of Jun 9, 2026, the average hourly pay for computational structural biology in the United States is $41.13, according to ZipRecruiter salary data. Most workers in this role earn between $30.05 and $48.56 per hour, depending on experience, location, and employer.

What are some common challenges faced by professionals in computational structural biology when collaborating with experimental scientists?

One common challenge is effectively communicating complex computational findings to experimental colleagues who may not be familiar with certain modeling techniques. Additionally, aligning computational predictions with experimental data can require iterative discussions to ensure that models are biologically relevant and experimentally testable. Successful collaboration often relies on building mutual understanding of each team's methodologies and limitations, which fosters productive interdisciplinary research.

What are the key skills and qualifications needed to thrive as a Computational Structural Biologist, and why are they important?

To thrive as a Computational Structural Biologist, you need a solid background in structural biology, molecular modeling, and bioinformatics, typically supported by a PhD or relevant advanced degree. Proficiency in programming languages (such as Python or R), molecular visualization tools (like PyMOL or Chimera), and experience with structural databases are commonly required. Strong analytical thinking, collaboration, and communication skills help you interpret complex data and work effectively within multidisciplinary teams. These competencies are crucial for advancing scientific understanding and developing novel therapeutics in the field of structural biology.

What is computational structural biology?

Computational structural biology is a field that uses computer-based methods and simulations to study the three-dimensional structures of biological molecules, such as proteins and nucleic acids. Researchers use computational tools to predict molecular structures, analyze their dynamics, and understand how they interact with other biomolecules. This approach complements experimental techniques like X-ray crystallography and cryo-electron microscopy, helping scientists gain insights into biological function and design drugs or therapies. The field integrates knowledge from biology, chemistry, physics, and computer science.
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What are the most commonly searched types of Computational Structural Biology jobs? The most popular types of Computational Structural Biology jobs are:
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Computational Structural Biology jobs near you
Infographic showing various Computational Structural Biology job openings in the United States as of May 2026, with employment types broken down into 94% Full Time, and 6% Part Time. Highlights an 77% Physical, 2% Hybrid, and 21% Remote job distribution, with an average salary of $85,546 per year, or $41.1 per hour.

Computational Biologist

Transcripta Bio

Palo Alto, CA • On-site

Full-time

Posted 14 days ago

Job description

About Transcripta Bio
Transcripta Bio is a preclinical-stage AI drug discovery company pioneering a patient-first approach to therapeutics. Headquartered in Palo Alto, CA, we have built a proprietary closed-loop discovery engine - comprising our Disease Signature Atlas, Drug-Gene Atlas, and Conductor AI platform - that integrates single-cell patient transcriptomics, causal human genetics, and pre-validated chemistry to identify and advance drug candidates with a structural edge over conventional approaches.
We are looking for a Senior Scientist to become a cornerstone of our wet lab operations. You will own key areas of our experimental platform - from cell culture and high-throughput drug screening to the downstream assays that validate hits and guide program decisions. This is a hands-on role with real scientific ownership, where your work directly shapes the data that powers our discovery engine.
WHAT YOU'LL DO
  • Develop, maintain, and optimize reproducible bioinformatics pipelines for processing, QC, and analysis of high-throughput datasets, including bulk RNA-seq, single-cell RNA-seq, and high-content imaging data.
  • Analyze data from drug perturbation screens to identify transcriptomic signatures, compound-gene associations, and patterns of drug response across disease-relevant cell models.
  • Integrate data across multiple experimental modalities (transcriptomics, imaging, protein measurements) to build a coherent picture of biology and prioritize therapeutic hypotheses.
  • Partner with wet lab scientists to help design experiments, define data standards, troubleshoot data quality issues, and ensure clean handoffs between experimental and computational workflows.
  • Contribute to the curation and expansion of the Drug-Gene Atlas: ensure that data inputs are well characterized, analysis methods are calibrated, and outputs are interpretable and reliable.
  • Communicate findings clearly through reports, visualizations, and presentations to both computational and non-computational colleagues.
  • Stay current with advances in transcriptomics, single-cell methods, and computational biology; evaluate and adopt new tools and approaches where they add value.
  • Contribute to code review, documentation, and best practices as the team grows.

WHAT YOU'LL BRING
  • PhD in Bioinformatics, Computational Biology, Genomics, or a related field with 3+ years of relevant experience in industry.
  • Extensive hands-on experience processing and analyzing bulk and/or single-cell RNA-seq data, from raw reads through QC, normalization, dimensionality reduction, clustering, and differential expression.
  • Experience in relevant scientific packages (e.g., scanpy, pandas, numpy, DESeq2, ggplot2) and comfort working in a Linux/command-line environment. Strong programming proficiency in Python and/or R is a plus
  • Experience building and running reproducible workflows using tools such as Snakemake, Nextflow, or equivalent; familiarity with version control (Git) and best practices for collaborative code development.
  • Exposure to high-throughput or perturbational screening datasets (chemical, genetic, or combined) is highly desirable.
  • A biologically grounded mindset: you approach data with mechanistic questions in mind, not just statistical outputs.

NICE TO HAVE
  • Experience analyzing data from functional genomics assays (e.g., ATAC-seq, ChIP-seq, perturb-seq, or pooled CRISPR screens).
  • Familiarity with spatial transcriptomics or multimodal data integration approaches.
  • Experience working with or alongside ML/AI teams; familiarity with applying machine learning methods to biological data.
  • Background in rare genetic disease, neurodegeneration, or other genetically defined disease areas.
  • Experience in cloud-based compute environments (AWS, GCP, or equivalent)

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