Leonhard Gruenschloss
AI And Machine Learning Software Engineer at Victorian Clinical Genetics Services (VCGS)
Hobart, Tasmania, Australia
Summary
👤
Senior🎓
Top SchoolLeonhard Gruenschloss is an AI and machine learning software engineer with 8 years of experience applying scalable cloud-native systems to genomics, utilities, and planet-scale monitoring. He currently accelerates rare disease diagnosis at VCGS by translating ML research into clinical workflows and working directly with variant curators to shorten the path to patient impact. Previously he led cloud genomics infrastructure at the Garvan Institute—building an open-source stack that processed tens of thousands of whole genomes—and drove network-model ML for country-scale LiDAR at Neara. His background at Google includes leading teams for fleet monitoring, OpenTelemetry, and Spanner, giving him deep expertise in distributed systems, observability, and production reliability. Early R&D roles in photorealistic rendering and quasi-Monte Carlo methods inform a strong foundation in high-performance computing and numerical methods. He is a pragmatic engineer who pairs research-grade algorithms with production DevOps—contributing to prominent open-source projects like Hail to improve cloud genomics tooling and stability.
8 years of coding experience
17 years of employment as a software developer
M.S. Computer Science / German Diplom Computer Science, M.S. Computer Science / German Diplom Computer Science at Ulm University
German, English
Github Skills (13)
docker10
python10
dockers10
batch-processing10
gcp10
devops9
bioinformatics9
cloud-infrastructure9
database-management8
kubernetes8
kubernetes-pods8
apidoc7
api7
Programming languages (18)
Github contributions (5)
hail-is/hail
Jan 2021 - Oct 2022
Cloud-native genomic dataframes and batch computing
Role in this project:
Back-end & DevOps Engineer
Back-end & DevOps EngineerContributions:48 reviews, 23 commits, 39 PRs in 1 year 9 months
Contributions summary:Leonhard contributed to the back-end infrastructure and deployment configurations for Hail, a cloud-native genomic data processing platform. Their work included modifying Docker image handling for Google Cloud and Artifact Registry, adjusting authentication mechanisms for batch processing, and implementing changes to the service backend initialization. Furthermore, the user made enhancements to the `hailctl` command-line tool, and adjusted settings for database connections. These contributions indicate a focus on improving the platform's stability, scalability, and integration with cloud services.
scalabledata-analysispythongeneticshail
populationgenomics/cuKING
Jun 2022 - Mar 2025
CUDA-based KING relatedness estimation
Contributions:1 release, 1 review, 27 PRs in 2 years 9 months
cudaldagpusvdestimation
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.