Join a startup transforming scientific research through cutting-edge data systems as a Full Stack Engineer. You will develop core data infrastructure and collaborate with scientists to convert domain-specific needs into software solutions. The ideal candidate has 1-4 years of experience in full-stack software engineering with a strong foundation in data infrastructure.
Key Highlights
Key Responsibilities
Technical Skills Required
Benefits & Perks
Nice to Have
Job Description
About the job
This role is being recruited by CoffeeSpace on behalf of an innovative startup dedicated to transforming scientific research through cutting-edge data systems.
Join a team already making waves in the world of scientific research by providing essential infrastructure to connect and analyze lab data like never before.
Location: San Mateo, California
Compensation: $140K–$200K base + 1.00%-3.00% equity
Employment type: Full-time
Visa: Open to visa transfers and sponsorships (e.g. OPT, H1B transfers)
About the company
This emerging startup focuses on breaking down data silos within research labs. Their platform connects diverse instruments and spreadsheets, enabling scientists to utilize modern AI models for cross-experimental analysis and gain unparalleled visibility into their data.
The company's mission is to empower labs to develop tomorrow's technology by improving data infrastructure, thus reducing the time spent wrangling data and allowing more focus on groundbreaking science.
They are already collaborating with academic institutions and national labs, with a growing customer base and demand that is outpacing their current capacity.
About the role
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As a Full Stack Engineer, you will be instrumental in developing core data infrastructure, encompassing tasks from data ingestion to storage and indexing. You'll work across the stack, leveraging your skills in Python for backend development and TypeScript/Next.js for frontend solutions.
You'll collaborate closely with scientists to convert domain-specific needs into functioning software, ensuring reliability while balancing quality and speed. This role promises a unique chance to influence the future of scientific research by enhancing the tools scientists rely on.
What You’ll Do
- Build and maintain robust data infrastructure facilitating data ingestion, normalization, and storage.
- Design schemas for querying heterogeneous lab data effectively.
- Develop full-stack software with significant ownership, using Python and Next.js.
- Engage with scientific professionals to transform complex problems into practical software solutions.
- Play a key role in ensuring reliability while making quality trade-offs as needed.
Ideal Candidate
- 1-4 years of experience in full-stack software engineering, with a strong foundation in data infrastructure.
- Proficient in Python, TypeScript, SQL, and ideally, knowledge of Django and NoSQL systems.
- Demonstrated experience in backend architecture and system-level thinking.
- A background or interest in scientific research is a significant asset.
- Capable of working onsite in San Mateo with minimal remote flexibility.
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Why Join Us?
- Become a crucial part of an ambitious startup that’s already impacting scientific research workflows at a national level.
- Collaborate with top-tier scientific labs and institutions, contributing directly to the tools that enable scientific breakthroughs.
- Enjoy the challenge of working in a dynamic environment where your contributions make an immediate difference.
Next steps
- Apply via this LinkedIn job post.
- We’ll review your application and reach out if there’s a strong fit.
- Successful candidates will be introduced directly to the team.
- If this role is not the perfect fit, we may suggest other opportunities, always with your consent.
A quick note on authenticity
This is a real, active role that CoffeeSpace is recruiting for in close partnership with the hiring team. We don’t post speculative roles and work directly with teams on their actual hiring needs.
Join us to help reshape the data infrastructure that underpins scientific discovery.
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