Senior Full Stack Software Engineer
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Before you apply
Salary$180k – $240k
LocationSan Mateo, CA
Typefull-time
SenioritySenior
Experience4+ yrs
DepartmentGeneral
EquityIncluded
Company size2+ people
First seenOct 16, 2026 · 2w ago
Verified live2d ago
At a glanceSummarised by Seekless from the posting.
4+ years of software engineering experience
Strong in Python and TypeScript
Know databases deeply (not just how to use them, how they work)
Comfortable independently solving problems end-to-end
Curious about science and how labs actually operate
CS degree or equivalent
Have experience with scientific data, Django, Next.js, GCP, SQL, early-stage startups
Work visas will be considered on a case-by-case basis
Skills
Python
TypeScript
Next.js
Django
GCP
SQL
React
The work
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Build core data infrastructure: ingestion, normalization, storage, indexing
•
Ship across the stack (Python backend, TypeScript/Next.js frontend)
•
Design schemas that make heterogeneous lab data queryable
•
Work with scientists to turn domain problems into working software
•
Own reliability and make good tradeoffs on quality vs. speed
You
•
4+ years of software engineering experience
•
Strong in Python and TypeScript
•
Know databases deeply (not just how to use them, how they work)
•
Comfortable independently solving problems end-to-end
•
Curious about science and how labs actually operate
•
CS degree or equivalent
•
[optional] Have experience with scientific data, Django, Next.js, GCP, SQL, early-stage startups
Benefits
Competitive base with flexible salary/equity split. ($180K - $240K, 0.50% - 2.00%)
Work visas will be considered on a case-by-case basis.
About the company
About Labric
Effective use of AI will transform scientific research, but most lab data is stuck in silos. Scientists can’t ask basic questions across their own experiments. Even the best tools can’t help with what they can’t see.
Labric builds the missing layer. We connect instruments, spreadsheets, and other data sources to a central platform, transforming data into useful visualizations and metrics. Scientists use it for cross-experimental analysis, visibility into colleagues’ work, and application of the latest AI models.
We’re working with companies, academic labs, and DOE national labs, and demand is outpacing our team. This is a rare chance to rebuild the tools the world’s best scientists use to create tomorrow’s technology.
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