Senior Bioinformatics Engineer

Senior Bioinformatics Engineer

Compensation: $125K – $200K base 0.25% - 0.5% equity

About BiomeSense

BiomeSense is a pioneering life sciences startup focused on harnessing the power of the gut microbiome to revolutionize personalized medicine. With our breakthrough technologies GutLab and MetaBiome, we are setting new standards in microbiome biomarker discovery, data analysis and interpretation, enabling unprecedented insights to understand & leverage the microbiome for human health. We partner with leading institutions and researchers including UCSF, UCSD, NIH, and the University of Chicago, and are backed by investors such as LabCorp Ventures, Bluestein Ventures, Seerave Foundation, and Emil Capital Partners.

The Position

You’ll help build our MetaBiome platform, a novel software solution for collection, curation and analysis of longitudinal microbiome & health data at scale. This role provides a unique opportunity to be one of the earliest employees at a rapidly growing start-up, contributing across bioinformatics, data engineering, and product development. We’re looking for someone who is excited to build - and someone who has the character and drive to grow into a platform leader over time. You’ll join near the ground floor of development, getting hands-on experience building the infrastructure, pipelines, and novel analytical workflows to turn raw microbiome data into actionable and high-integrity microbiome intelligence.

Key Responsibilities:

• Bioinformatics Architecture & Infrastructure - help design, implement, and maintain the robust, auditable backend required for the highest-quality extraction of clean microbiome data from raw shotgun metagenomic sequencing data.
• Data Engineering – contribute to the development and maintenance of cloud-based infrastructure, including databases, distributed computing workflows, and REST endpoints, supporting a data platform that organizes microbial sequence data and associated clinical metadata.
• Statistical Analysis – lead the design, execution, and documentation of reproducible computational experiments from concept to conclusion, including experimental design, data preparation, model selection, and evaluation strategies.
• Methods Development – Develop and implement novel analytical methodologies and algorithms informed by experimental findings and validated through rigorous evaluation.
• Frontend / Backend Development – design, implement, and maintain robust data ingestion interfaces leveraging external APIs and clinical ontologies such as SNOMED, MedDRA, and ICD-10, adhering to software engineering best practices including wireframe design, unit testing, version control, code review, and security and compliance.

Qualifications

Required

• B.S. /M.S. /PhD in Computational Biology, Bioinformatics, Computer Science or a related field with 3 yrs industry experience (biotech start-up ideal)
• Strong proficiency in Python and JavaScript, with experience building scalable, reusable, testable, and maintainable software.
• Proficiency with PostgreSQL or other relational SQL database systems.
• Familiarity with clinical ontologies such as SNOMED, ICD-11, ATC, and LOINC.
• Familiarity with Amazon Web Services and best practices in computer security.
• Proficiency with one or more modern frontend web technologies.
• Excellent communication skills for presenting complex scientific concepts to diverse stakeholders.
• Strong ownership mindset and the ability to execute in fast-moving environments.
• Strong fundamentals in bioinformatics pipelines, QC, and reproducibility.

Strongly Preferred

• Bioinformatician with experience working with NGS data and bioinformatics pipeline verification and knowledge of microbiome-specific challenges (compositional data, variability, confounders).
• Familiarity with longitudinal / repeated-measures biological datasets.
• Experience integrating external ontologies or standardized vocabularies.
• Exposure to cloud infrastructure, workflow orchestration, or containerization.
• Interest in AI/ML applications built on large biological datasets.