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Director of Bioinformatics

Listing for: Dinamo Biotechnologies
Full Time position Listed on 2026-11-09
Job specializations:
  • Software Development
    AI Engineer (Applied/Software), Machine Learning/ ML Engineer, Data Scientist
Salary/Wage Range or Industry Benchmark: 180000 - 320000 USD Yearly USD 180000.00 320000.00 YEAR

Job Description & How to Apply Below

Dinamo Biotechnologies is a Series A precision medicine company, backed by True Ventures and Greenfield Partners, focused on autoimmune and inflammatory disease. Our platform combines a high-resolution, whole-genome epigenetic readout with machine learning to decode the epigenome and identify the patients most likely to respond to therapy. We apply it to clinical-stage programs to enable precision, biomarker-driven development — computational science sits at the center of the company.

Dinamo Biotechnologies is a Series A precision medicine company, backed by True Ventures and Greenfield Partners, focused on autoimmune and inflammatory disease. Our platform combines a high-resolution, whole-genome epigenetic readout with machine learning to decode the epigenome and identify the patients most likely to respond to therapy. We apply it to clinical-stage programs to enable precision, biomarker-driven development — computational science sits at the center of the company.

The Role

The Director of Bioinformatics will own Dinamo's computational strategy end to end — from raw methylation sequencing data to validated, regulatory-grade responder signatures. This leader sets the scientific and technical direction, builds and leads a team spanning pipeline engineering, algorithms, and AI, and stays hands-on in the hardest problems. The role partners closely with molecular biology, clinical, regulatory/CDx, and business development, and is a visible member of the leadership team in program strategy, partner discussions, and investor communication.

Key Responsibilities Scientific & technical leadership
  • Define and execute the computational roadmap for methylation-based responder stratification across autoimmune indications (e.g., RA, lupus, IBD, psoriasis)
  • Devise novel methods and tools — statistical, ML, and AI/deep learning — to extract robust signatures from high-dimensional, high-noise epigenomic data with limited sample sizes
  • Set the standard for analytical rigor: pre-specified analysis plans, locked models, held-out and independent validation, benchmarking, and resistance to overfitting
  • Drive integration across data types and platforms (sequencing and array data, cohort and batch effects, domain adaptation) and with clinical endpoints
  • Own production-grade, reproducible pipelines from primary/secondary processing (alignment, methylation calling, QC) through tertiary analysis and model deployment
  • Build scalable cloud/HPC infrastructure, data management, and version control suited to CLIA-ready and companion diagnostic development
  • Evaluate and manage external partners (sequencing providers, AI/bioinformatics CROs) and new technologies
Team building
  • Recruit, lead, and grow a high-performing team covering bioinformatics pipelines, algorithm development, and ML/AI engineering
  • Establish engineering practices that let a small team move fast without losing rigor
  • Mentor scientists and engineers; create a culture of ownership, candor, and scientific integrity
Qualifications
  • Ph.D. in bioinformatics, computational biology, statistics, computer science, or a related field
  • 8+ years in computational biology/bioinformatics, including 3+ years leading teams in an industry setting
  • Deep expertise in genomics and epigenetics, with hands-on DNA methylation experience (WGBS, EM-seq, TAPS, arrays, and/or cfDNA methylation)
  • Track record of original computational contributions — products, publications, patents, or open-source tools — solving hard problems in high-dimensional omics data
  • Fluency in Python; strong command of cloud/HPC environments and workflow managers (e.g., Nextflow, Snakemake, WDL)
  • Hands-on experience with AWS infrastructure (e.g., EC2, S3, Batch) and cost-aware scaling of compute-intensive genomics workloads
  • Strong…
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