Scientific Software Developer
Austin
Crinklaw
7+ years bridging software engineering and bioinformatics in biotech R&D. Building full-stack platforms, AI-driven discovery tools, and computational pipelines for therapeutic development.
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RISC Complex · PDB 4W5N · drag to rotate
Experience
Empirico Inc
March 2023 — Present- ·Architected the company's primary LIMS system (NextJS/React, FastAPI, PostgreSQL) serving 30 scientists, tracking mission-critical data across siRNA selection, synthesis chemistry, reagent inventory, and in-vivo studies
- ·Led a team of 2 engineers, driving mentorship and cross-functional collaboration with wet lab researchers
- ·Developed an agentic target discovery platform leveraging AI/LLMs to accelerate therapeutic research
- ·Designed integrated data pipeline feeding experimental results into models to predict siRNA efficacy
- ·Established CI/CD pipelines, containerized deployments, and testing standards for team infrastructure
Jumpcode Genomics
March 2022 — March 2023- ·Implemented pipelines (Python/Snakemake) to design CRISPR/Cas9 gRNA sequences; adopted across all R&D products
- ·Overhauled AWS infrastructure to reduce compute costs by 50%; managed general administration and provisioning
1859 Inc
September 2021 — March 2022- ·Developed automated NGS pipeline (Python/Nextflow) for DNA encoded libraries
- ·Created web apps (React, Flask, Celery) enabling scientists to trigger analyses and visualize results
LJI, Peters Lab
February 2018 — September 2021- ·Implemented and optimized a method for determining T-cell receptor similarity and epitope specificity; prototyped in Python, optimized in C++. Co-first author
- ·Created Python package classifying protein sequences into TCR, Antibody, or MHC using Hidden Markov Models
Projects
TCRMatch
C++ · Computational Immunology
CLI tool for T-cell receptor specificity prediction. Determines TCR similarity and epitope specificity using an optimized scoring algorithm. Co-first author publication.
RustMelt5
Rust · Bioinformatics
Fast library for calculating oligonucleotide melting temperatures. Built for performance-critical bioinformatics workflows.
ARC
Python · Bioinformatics
Package for classifying protein sequences into TCR, Antibody, or MHC categories using Hidden Markov Models.
Skills
Languages
Full Stack
Bioinformatics
Infrastructure
Education
University of California, San Diego
Bachelor of Science, Bioinformatics