Senior Bioinformatics Engineer

Diploid Genomics•San Diego, CA

1d•Hybrid

About The Position

The Senior Bioinformatics Engineer works at cohort scale — systematically analyzing large collections of complete, personal human genomes across all variant types, including structural variation and difficult-to-sequence regions, and building the high-performance platform and applications that make that analysis possible. Operating as a senior individual contributor, the successful candidate pairs strong software-engineering practice with scientific rigor to turn assembly-based, cohort-scale genome data into reliable research and diagnostic capabilities. This is a hands-on, application-driven role. The successful candidate designs and builds efficient, reproducible tools and pipelines; drives research and diagnostic applications such as biomarker discovery, missing-heritability studies for complex disease, and oncology analysis; and applies AI and machine-learning methods to process and interpret large cohorts more efficiently. They bring disciplined engineering, careful validation, and scientific rigor to everything they build, make and defend sound technical choices with limited oversight, and thrive amid the ambiguity of a fast-moving startup environment, working closely with peers across the team.

Requirements

M.S. or Ph.D. in Bioinformatics, Computational Biology, Computer Science, Genomics, or a related discipline — or equivalent practical experience.
5+ years of hands-on experience (or Ph.D. plus 3+ years) in large-scale genome analysis, including whole-genome and pangenome projects.
Extensive prior experience in genome research (not limited to human genomes), and a willingness to apply and extend those skills — and learn new techniques — for human-scale genome analysis.
Proven ability to scope, design, build, and operate production-grade, end-to-end bioinformatics pipelines with minimal guidance, taking accountability for their correctness, scalability, reproducibility, and performance in high-throughput and high-performance computing and cloud environments.
Deep, current understanding of state-of-the-art bioinformatics algorithms across genome assembly, variant calling, structural-variant calling, haplotype phasing, and pangenome analysis, with the judgment to select, adapt, and benchmark them appropriately rather than applying them off the shelf — the level of expertise the team can treat as a go-to resource.
Strong software engineering foundations: proficiency in Python (and ideally a compiled language such as C/C++, Rust, or Go), version control, automated testing, containerization (Docker / Singularity), and workflow managers (Nextflow, Snakemake, or WDL).
An independent generalist who thrives in a startup environment — comfortable working with minimal direction across a broad range of tasks, taking ownership of problems from start to finish, and adapting quickly to a fast pace and rapidly changing requirements while prioritizing the most immediate needs.
A track record of elevating a team by developing others — using prior experience to mentor entry-level and mid-level staff toward independent, high-quality work through teaching, design and code review, and stepping in directly only when necessary — rather than through direct management.

Nice To Haves

Hands-on experience with long-read sequencing (PacBio HiFi, Oxford Nanopore) and modern pangenome tooling (e.g., minigraph-cactus, vg, pggb).
Experience in oncology / cancer genomics, immunogenomics, neoantigen prediction, HLA or immune-receptor biology, or pharmacogenomics.
Experience with cloud platforms (AWS / GCP) and optimizing genome-scale workloads for cost and performance.
Contributions to open-source bioinformatics tools or methods.
Experience supporting regulated, clinical, or translational genomics programs.

Responsibilities

Cohort-Scale Genome and Variant Evaluation: Systematically evaluate large cohorts of human genomes across all variant types — SNVs, indels, structural variants, and other complex events — derived directly from personal, de novo genome assemblies, with particular attention to difficult-to-sequence and difficult-to-map regions. Develop and apply metrics and quality frameworks to assess the accuracy, completeness, and consistency of variant calls across the cohort.
High-Performance Cohort Genome Analysis Platform: Contribute to the engineering work of building a high-performance platform for cohort-scale, complete-genome analysis, and assist with its maintenance and continuing improvement. Help keep the platform scalable, reproducible, and efficient as cohort size and data volume grow.
Research and Diagnostic Applications: Drive research and diagnostic applications built on extensive cohorts of complete human genomes, supporting biomarker discovery, missing-heritability studies for complex disease, and oncology applications. Collaborate with scientists, clinicians, and cross-functional teams to translate cohort-scale findings into research insight and diagnostic value.
AI for Genomics at Cohort Scale: Apply AI and machine-learning methods to genomics to improve the efficiency of processing and interpreting large cohorts, working closely with peers across the team. Explore and evaluate AI approaches that help extract biological and clinical insight from complete-genome cohort data at scale.
Applied Research and Continuous Improvement: Conduct scientific study and review the most recent literature, and drive toward applications by turning promising methods and findings into practical tools, analyses, and improvements. Identify inefficient algorithms or code and improve them when necessary, performing analyses to evaluate and compare alternative approaches on representative data. Adopt and adapt new computational technologies and software-engineering tools and practices to keep the platform, methods, and codebase current, efficient, and maintainable.