The posting
About Claryx Claryx is building the hospital immune system, reading the genomes of confirmed patient infections, linking related cases as outbreaks, and tracing each back to its likely source. When that source is in the building (a sink, a shared piece of equipment, a particular room), Claryx finds it by matching the patient’s strain against a digital twin of the hospital’s microbiome, sequenced from its air, its water and its surfaces, all on proprietary chemistry cheap enough to run continuously. Founded by brothers Kurt and Dirk Hackenberger, Claryx is based in New York City and is live across multiple health systems.
The Role We’re hiring a Computational Scientist to take over Claryx’s inference platform: the models that turn sequence data from patient infections and environmental samples into a defensible claim about what came from where, and when. The list below is in priority order for the first year, with a specific focus on modelling and inference. The platform and security posture underneath it come with the role, but Drata supports them, and we don’t expect you to arrive with deep expertise. We’re early-stage, so whoever takes this role will set technical direction rather than follow it and use existing agentic coding tools to maximize output.
Key Responsibilities Phylogenetic inference. Time-resolved trees, structured coalescent and birth–death models, ancestral state reconstruction, recombination-aware alignment, across clinical and environmental samples together. Bioinformatics pipelines. Running and hardening our metagenomic and comparative genomics pipelines, from QC through strain-level comparison between clinical isolates and environmental samples. Transmission attribution. Predict transmission pathways under incomplete sampling over a facility represented as a network of reservoirs. Metagenomic inference. Strain-level deconvolution of mixed environmental populations. Cloud infrastructure. AWS environment. Solidify infrastructure-as-code, proper CI/CD, predictable costs. Compliance. Deepen HIPAA/SOC 2 controls to maintain compliance and remain audit-ready. Results communication. Move reporting off static outputs onto an interactive dashboard.
Qualifications PhD in mathematical biology. Population genetics, molecular evolution, biostatistics, genomic epidemiology. Mathematics. Deep knowledge of Likelihood and Bayesian inference, MCMC, continuous-time Markov chains, coalescent and birth-death processes, identifiability, model selection, etc. Machine and deep learning. Understand backpropagation and optimization. Simulation-based inference and posterior estimation. GNNs and other network representations are a plus. Phylogenetics. BEAST, IQ-TREE or equivalents. Transmission-tree tools are a plus. AWS, Linux/bash, Git. Nextflow preferred for workflows. AWS in production – IAM, S3, Batch, Terraform. Agentic coding tools. Structuring problems, managing context, reading generated code critically.
Practicalities For strong performers, this role is designed to grow into our Head of Computational Science, expanding across the inference and data stack and eventually taking on a team. You’d report to the CTO and work closely with our molecular epidemiologist and the wet lab. It’s fully onsite in Long Island City, in the same space as the lab generating the data. We publish and work with academic groups, nonprofits and public health labs, and we'd want you publishing too, so the methods you build here end up in the literature as well as in production.
