San Mateo, CA
4 months ago

Job Overview

Job Type
Salaried, Full-Time
Pay
Competitive base with flexible salary/equity split. ($180K - $240K, 0.50% - 2.00%)

Job description

Salary:
USD 180,000 - 240,000 per year
Location:
San Mateo, CA
Work arrangement:
On-site

Role Summary

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.

The work

  • 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

Work visas will be considered on a case-by-case basis.

Role:
Senior Full Stack Software Engineer
Job Type:
Salaried, Full-Time

Company profile

Labric

labric.co

Labric is a data infrastructure platform for scientific research labs. Labric automatically captures and structures instrument data, giving researchers AI-ready datasets for analysis and giving decision-makers complete visibility, and it works across scientific disciplines. Created by Stanford CS graduates who had experienced the chaos of lab data, the company's mission is to turn fragmented research data into structured infrastructure that saves research teams hundreds of hours.

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