Senior Bioinformatician - Genomics Data Infrastructure

About Violet Research Institute Violet Research Institute (VRI) is building the future of personalized medicine for patients with genetic diseases. We combine the urgency and execution mindset of a startup with the mission‑driven openness of a nonprofit, bringing together leading researchers, engineers and organizations across omics, therapeutic design, manufacturing, clinical care and AI to move from insight to action as quickly as possible. Location San Francisco Bay Area (or Remote, US‑based preferred) Type Full‑Time Compensation $175k – $225k Role Overview As the founding Bioinformatician you will architect and own VRI’s entire genomics data foundation, from sequencing data ingestion to quality control, storage, and analysis readiness. You will design scalable, reproducible pipelines and data infrastructure that enable rapid, reliable clinical interpretation. Your work will directly impact real‑patient outcomes. What You’ll Own Genomics Data Management & Stewardship Own the full lifecycle of VRI’s genomics data, from raw sequencer output (FASTQ, BAM/CRAM, VCF) through QC, storage, versioning, and retrieval. Define and enforce data standards, naming conventions, metadata schemas and ontologies for all data types. Build and maintain a centralized, queryable genomics data lake that unifies heterogeneous inputs from internal labs and CRO partners. Establish sample tracking, data lineage documentation, and versioning protocols so every result is traceable back to its source. Manage cloud storage strategy (AWS S3 or GCP) across hot, warm, and cold tiers, balancing cost, accessibility and HIPAA‑compliant security. Create and maintain an internal data catalog documenting all datasets, pipeline versions, and transformation logic. Design and build production‑grade, reusable pipelines for ingesting and processing PacBio long‑read WGS data, including phased genome assembly, structural variant calling and SNP/indel detection. Build ETL workflows that clean, normalize and integrate diverse data modalities into unified, analysis‑ready formats. Automate QC steps to surface data anomalies early and monitor data quality continuously across sequencing batches and CRO handoffs. Establish code quality standards, testing protocols, and deployment practices (version control, containerization) that will scale as the team grows. Maintain and develop internal database systems, including our proprietary VRI OS platform used for experiment tracking. Integrate physics‑based thermodynamic models and predictive algorithms to forecast therapeutic performance and guide design decisions. Develop and apply design criteria and ranking systems to evaluate therapeutic candidates computationally before advancing to wet‑lab testing. Build and maintain algorithms that bridge computational predictions with experimental validation, optimizing the design‑to‑testing pipeline. Multiomics Integration Integrate genomics data (DNA, RNA‑seq, long‑read RNA, splicing) with proteomics, metabolomics and mass‑spectrometry data into coherent, patient‑centric multiomics datasets. Query and harmonise large‑scale population cohorts (UK Biobank, Mount Sinai Million and similar) to contextualise patient findings. Partner with computational biologists and clinical scientists to surface analysis‑ready datasets, enabling and supporting their interpretation work. Insight Delivery & Reporting Build automated reporting pipelines that push structured summaries of data quality, pipeline status and batch results to scientific stakeholders. Develop QC dashboards to surface data quality metrics, pipeline status and anomaly alerts in real time. Support IND filings through preparation of relevant datasets and written reports. Monitor the bioinformatics landscape, identifying emerging algorithms and platforms that can sharpen VRI’s data infrastructure. Lay the foundation for future bioinformatics hires by embedding well‑documented, reproducible data practices from day one. Qualifications Must Have 4+ years of hands‑on bioinformatics experience in a research or biotech environment, focused on genomics data management and pipeline engineering. Experience owning genomics data end‑to‑end – building systems and standards that make data trustworthy and reusable. Strong fluency in genomics file formats and toolchains (FASTQ, BAM/CRAM, VCF, BED; GATK, DeepVariant, PBSV; hifiasm or equivalent). Demonstrated experience with PacBio long‑read WGS data and associated tooling. Proficiency in Python and production‑grade pipeline development with workflow managers (Nextflow, Snakemake, or WDL). Hands‑on experience with cloud data infrastructure (AWS S3 or GCP), data lake design, pipeline orchestration and HIPAA‑compliant storage. Experience querying and integrating biobank‑scale datasets (UK Biobank or similar). Strong organisational skills – you document and build systems others can use, taking ownership of data quality without being asked. Preferred Experience with RNA‑seq and long‑read RNA analysis, including pre‑mRNA processing and splicing characterisation. Familiarity with LIMS systems (Benchling, LabVantage or similar) and data governance / FAIR data frameworks. Experience with containerisation tools (Docker, Singularity) and CI/CD practices. Exposure to siRNA, ASO or other therapeutic modality‑specific bioinformatics. Experience in a seed or early‑stage biotech; comfort building infrastructure from scratch. Ability to execute independently from loosely specified tasks; you are self‑directing. Clear communication of challenges and progress when seeking help. Thrives in early‑stage, ambiguous, high‑pace environments. Mission‑driven with genuine, active care for patient impact. Benefits We offer competitive compensation, a full suite of benefits, and a culture that embraces AI across all functions to accelerate discovery and impact. #J-18808-Ljbffr Violet Research