Engineering better biology through artificial intelligence
Orbit Labs builds computational tools, laboratory robotics, and machine learning systems that support biological and environmental research — from genome analysis to freshwater biodiversity monitoring.
Biological research generates more data than any single lab can interpret by hand. Orbit Labs designs the software, models, and robotics that help researchers process that data faster and more reliably — without replacing the scientific judgment behind it.
We work across computational biology, environmental monitoring, and laboratory automation, operating as a research division of Orbit AI. Our output is code, models, instruments, and published methods — not consumer products. Everything below distinguishes between what is running today, what is being tested, and what remains a longer-term goal.
Seven disciplines, one shared data layer
Each group works independently but shares infrastructure — the same sequencing pipelines, compute cluster, and model registry — so findings in one discipline can be reused in another.
The platforms our research runs on
Four systems used daily across the organization. None of them are external products — they exist to serve Orbit Labs' own research.
Compute cluster
An on-site HPC cluster with cloud burst capacity for genome assembly, molecular simulation, and model training workloads.
Model registry
A versioned store of sequence and structure models, so every discipline can audit which model version produced a given result.
Sample-prep robotics
Automated liquid handling for library preparation, reducing hands-on time and improving run-to-run consistency ahead of sequencing.
Data ingestion pipeline
Standardizes and quality-checks raw sequencer and sensor output before it enters shared storage, with full provenance tracking.
Continuous freshwater biodiversity monitoring
A joint effort between our Environmental DNA and Laboratory Robotics groups, shown here at its actual stage of progress.
From manual sample collection to automated, continuous monitoring
Traditional biodiversity surveys rely on researchers manually collecting water samples and shipping them to a lab for sequencing — a process that can take weeks. This project tests whether that turnaround can be shortened using portable sequencing and on-site sample processing, without compromising the accuracy of species detection.
How decisions get reviewed
Two separate but related structures: who is accountable for the organization, and what principles constrain the research itself.
01Independent scientific advisory board
Reviews research direction and methodology on a recurring basis, separate from day-to-day management.
02Biosafety committee
Approves any work involving live organisms or environmental sample handling before it begins.
03Data governance lead
Owns access controls and retention policy for genomic and environmental datasets.
04Reporting line to Orbit AI
Orbit Labs operates as a research division, with budget and risk oversight from its parent company.
01Sample and site consent
Environmental sampling only occurs with landowner or regulatory authorization on record.
02Open methodology
Pipeline code and model architectures are documented internally in a form suitable for external review on request.
03No dual-use research
Projects with plausible dual-use biosecurity risk are declined at the proposal stage.
04Honest stage reporting
Every project states whether a result is established, experimental, or aspirational — the same convention used on this site.
Built by a small number of specialists, not a large generalist team
We hire for depth in a specific discipline first. Most roles sit inside one research group and collaborate with the shared infrastructure teams as needed.
Open positions are shared through direct outreach and our contact form — check the Careers inquiry type below.
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