Vivodyne creates human data before clinical trials.
We accelerate the successful discovery, design, and development of human therapeutics by testing on large, lab-grown human organ tissues at massive scale, driving technological advancement at the convergence of novel biology, robotics, and AI. We identify and validate new therapeutic targets and de-risk new therapeutic assets by producing clinically translatable multi-omic data from our proprietary, physiologically-realistic human organ tissues at unprecedented scale, speed, and quality. This enables us to produce more human data than all clinical trials in the U.S. combined. We’re financially backed by some of the most selective and successful venture funds, and we have already partnered with a majority of the top 10 multinational pharmaceutical companies to discover and develop better, safer drugs and dramatically reduce the burden of animal testing.
Role
The Software & Data Science team at Vivodyne tackles some of the hardest problems at the intersection of human biology, large-scale data, and machine learning using uniquely rich imaging and multi-omics datasets from lab-grown human tissues.
We are expanding our capabilities to generate high-throughput -omics data across automated human tissue models, with a primary focus on single-cell and bulk transcriptomics, secretomics, and proteomics. We're looking for a Bioinformatician who can develop new analytical methods and build robust infrastructure to support them at scale.
You will define how we process, store, integrate, and interpret biological datasets, making foundational decisions that shape the long-term utility of our data for internal research, AI/ML model development, and external partners. This means owning both the science (what question does this analysis answer, and how defensible is the conclusion?) and the engineering (does this pipeline run reliably at scale, and can someone else extend it?).
This is a high-impact, cross-functional role. You'll sit within the Software & Data Science organization and collaborate daily with experimental biologists, AI/ML researchers, software engineers, and client-facing teams.
This is a full-time, onsite role in San Francisco.
Responsibilities
Develop and Own Analytical Methods
- Design, implement, and critically evaluate computational approaches for scRNA-seq, bulk RNA-seq, secretomics, and proteomics data including QC, normalization, batch correction, dimensionality reduction, clustering, differential expression, trajectory analysis, and multi-dataset integration.
- Go beyond applying standard tools: develop or adapt methods for perturbation modeling, causal inference, network reconstruction, ligand-receptor inference, and multi-omics integration as the science demands.
- Select and standardize tooling based on rigorous benchmarking, not convention. Challenge default choices when the data or use-case warrants it.
Build Scalable, Production-Grade Pipelines
- Architect end-to-end analysis workflows in Python and/or R using modern workflow orchestration (Nextflow, Snakemake, or equivalent) that are reproducible, version-controlled, and designed to run at the scale of tens to hundreds of thousands of samples.
- Partner with software and data engineers to productionize pipelines, ensure robust data ingestion and storage, and contribute to decisions on biological data architecture (storage formats, schema design, long-term data usability).
- Write well-documented, testable code that other engineers and scientists can run, review, and extend.
Drive Biological Insight and Interpretation
- Own dataset analysis to answer biological and client-driven questions with rigor. Ground conclusions in the limits of the data and clearly communicate what can and cannot be concluded with confidence.
- Partner with experimental biologists to frame the most relevant biological questions, design analyses, and recommend experiments and data-generation strategies to fill key gaps.
- Produce publication-quality figures, internal reports, and partner-facing deliverables that are decision-useful.
Shape Data Strategy
- Help define benchmarking strategies for internal tissue model development.
- Proactively identify opportunities to leverage internal and public datasets to accelerate scientific progress.
- Contribute to decisions around data standards, comparability, and the integration of omics features into downstream ML models and AI workflows.
Communicate Across Functions
- Translate complex multi-omic analyses into clear insights for biologists, AI/ML researchers, software engineers, and non-technical stakeholders.
- Tailor depth and framing to the audience while maintaining scientific rigor.
Requirements and Expectations
Technical Expertise
- Ph.D. in Computational Biology, Bioinformatics, Genomics, Systems Biology, Biostatistics, or a related quantitative field with deep focus on transcriptomics and/or proteomics.
- 2–5 years of post-PhD experience; some industry experience strongly preferred.
- Strong, hands-on proficiency in Python for bioinformatics (Scanpy and the broader scverse ecosystem in particular) with the judgment to know when existing tools are insufficient and the skill to build alternatives. Familiarity with R-based workflows (Seurat, Bioconductor, Monocle) is valued as evidence of breadth, but Python is the production language.
- Deep experience with scRNA-seq datasets and workflows, including QC, batch correction, clustering, differential expression, annotation, and integration across experiments.
- Experience with proteomics or secretomics analysis (Olink or similar high-dimensional panels), including normalization and cross-modality integration with transcriptomic profiles.
- Comfort in a Unix/Linux environment with standard dev tools (git, containers, CI/CD); experience with cloud environments (AWS) and workflow managers is a strong plus.
- Familiarity with data infrastructure for large-scale biological data (e.g., AnnData/H5AD, TileDB-SOMA, or similar) is a plus.
Problem Solving & Ownership
- Ability to independently structure and execute complex analytical projects from ambiguous initial questions to concrete, reviewable outputs.
- Strong biological intuition for distinguishing meaningful signal from artifact and the discipline to validate before interpreting.
- Comfortable navigating ambiguity in a fast-moving startup; can prioritize, make trade-offs, and hit agreed timelines.
Collaboration & Communication
- Excellent written and verbal communication; able to explain computational methods and biological implications to biologists, engineers, and non-technical stakeholders.
- Proven ability to collaborate across disciplines.
Innovation & Leadership
- Track record of taking omics analyses from raw data to figures/tables suitable for publications, reports, or decision meetings.
- Demonstrated ability to manage multiple analyses in parallel and ship work on predictable timelines.
- History of method development or meaningful adaptation of existing methods to novel data types or experimental designs.
Preferred Qualifications
- Experience with multi-modal data integration (e.g., combining imaging, transcriptomics, and proteomics features).
- Background in drug discovery, toxicology, or translational research where multi-omic data inform therapeutic decisions.
- Experience with representation learning or ML for biological data (embeddings for cells, genes, or proteins), especially in collaboration with ML specialists.
- Familiarity with organoids, primary tissues, or organ-on-chip systems.
Vivodyne, Inc. is an equal opportunity employer. Vivodyne complies with all laws respecting equal employment opportunity and does not discriminate against applicants with regard to any protected characteristic as defined by federal, state, and local law.
Compensation will be determined based on several factors including, but not limited to, skill set, years of experience, and the employee’s geographic location.
Pay Range$214,400—$245,000 USD