About the Role:As a Bioinformatics Engineer at Pilgrim, you'll develop methods and software to characterize sequencing data from ARGUS. You'll build metagenomic analysis pipelines that process reads as they arrive and run efficiently at the edge. You'll validate detection accuracy so operators can trust what ARGUS reports.
Responsibilities:- Develop and evaluate methods for agnostic detection and identification of organisms in metagenomic data, including mixed samples and low-abundance signals.
- Build sequencing pipelines covering basecalling, read quality control, classification, and reporting of detection evidence and uncertainty.
- Profile and optimize pipelines for ARGUS's onboard hardware, reducing latency and memory use.
- Benchmark detection performance on independent datasets, measuring false positives, false negatives, and time to detection.
- Version reference databases and maintain regression tests to catch performance changes after software or database updates.
- Investigate ambiguous findings with scientists, including contamination, preparation artifacts, and gaps or errors in reference data.
- Evaluate algorithms and machine learning methods against established approaches; deploy those that improve detection or computational performance.
Qualifications:- Strong foundation in bioinformatics, computational biology, or scientific computing. MS or PhD is a plus, not required.
- Hands-on experience analyzing sequencing data, including read quality assessment, alignment, or taxonomic classification.
- Proficiency in Python, C++, Rust, or a similar language for building bioinformatics software.
- Ability to use AI coding tools and independently review, test, and debug the code they produce.
- Experience benchmarking analysis methods with independent test data and measuring precision, recall, or false positive rates.
- Demonstrated improvements to runtime, memory use, or throughput on large datasets through software profiling and optimization.
Preferred:- Experience with nanopore sequencing, metagenomics, microbial genomics, streaming analysis, or deployment on resource-constrained hardware.
