Senior Software Engineer, Bioinformatics (Rust and Python)

Important Note: This is strictly a production software engineering role, not an academic research, data science, or scripting position. We are specifically looking for highly technical engineers with a strong Computer Science background who build robust, high-performance systems. If your primary experience is writing R or Python scripts for data analysis or academic research, this role will not be a fit., * Education & Experience: Master’s or Bachelor’s degree in Computer Science, Software Engineering, or a closely related technical field, backed by 5+ years of dedicated industry experience in production software engineering. \n

• Rust & Python Expertise: You are an expert in both Rust and Python. You understand Python internals, packaging, and design patterns, and you are highly proficient in writing memory-safe, concurrent Rust code (including Python extensions). \n

• Database Architecture: Strong background in database design, data modeling, and performance tuning. You have deep experience with MongoDB, relational databases (RDBMS), normalized schemas, and complex data migrations. \n

• Production Engineering Rigor: You do not write “scripts”; you build systems. You have deep expertise in the Software Development Lifecycle (SDLC), testing frameworks, mocking strategies, and writing modular, deployable code. \n

• Bioinformatics Domain Knowledge: Strong production experience working with standard genomic file formats (FASTQ / BAM / CRAM / VCF), variant calling algorithms, and high-throughput sequencing platforms (short- and long-read). \n

• Linux/Bash Mastery: You are highly comfortable debugging complex systems interactions in a Linux environment and writing robust shell automation. \n, * Advanced Degree: PhD in Computer Science, Software Engineering, or closely related field.

• High-Performance Engineering: Identify bottlenecks and rewrite/optimize core primary, secondary, and tertiary bioinformatics pipelines utilizing Rust for maximum performance and memory safety. \n

• Systems Integration: Seamlessly bridge high-performance Rust modules with our existing Python infrastructure using tools like PyO3 and Maturin. \n

• Database Architecture: Design, implement, and scale complex database schemas (MongoDB and RDBMS) to manage massive, high-throughput genomic datasets efficiently. \n

• Clinical-Grade Feature Development: Design, develop, validate, and deploy cutting-edge algorithms for variant detection and scoring on our high-performance CPU and GPU compute clusters. \n

• Production Quality Assurance: Enforce strict software quality standards, SDLC best practices, and automated testing to ensure our clinical results are perfectly accurate and reproducible in a highly regulated environment. \n