Software Engineer, Fullstack

Location: Bellevue, WA; Palo Alto, CA

Department: Engineering

About the Role

As a Founding Fullstack Engineer, you will report directly to the CTO and lead the development of our user-facing product. This is a high-impact role where you will hold a significant ownership stake in the company and lead the 0-to-1 build of our platform. You will work closely with our customers to translate their needs into a world-class product, while simultaneously shaping our engineering culture and technical direction from the ground up.

What You Will Do

• Build the Core Product: Own the end-to-end delivery of our user-facing platform, turning complex requirements into a clear, functional product-from UI components to the APIs and data models that support them.
• Architect for Scale: Design and implement a scalable frontend architecture capable of handling high-performance visualizations and massive datasets.
• Rapid Prototyping & Iteration: Build and refine new ideas quickly, maintaining a high bar for code quality, system performance, and UI/UX consistency.
• Define Engineering Culture: As a founding hire, directly influence the team’s technical standards, development workflows, and hiring processes.

Qualifications

• 5+ years of experience building production backend services (Python, Go, or Node.js) and APIs
• 2+ years of experience building web applications with React and TypeScript
• Experience with AWS or GCP in production environments
• Strong CS fundamentals and ability to make pragmatic system design tradeoffs
• Demonstrated ability to own features end-to-end, from scoping through deployment
• Ability to thrive in fast-paced, dynamic environments and navigate ambiguity.
• Bonus: Experience with ML pipelines, LLM fine-tuning, or data platform infrastructure

About the Company

About Harell Data

Harell Data is building the intelligent infrastructure for the next era of scientific discovery. While AI has revolutionized digital industries, progress in the physical sciences-from drug discovery to material science-is currently bottlenecked because the most valuable training data remains isolated, unorganized, and difficult to access.

We are solving this by providing an end-to-end ecosystem that enables the secure sharing of proprietary datasets alongside the high-performance compute and orchestration layer required to train large-scale models. By bringing compute to the data within a single, integrated environment, we are creating the foundational stack that transforms raw scientific data into the next generation of domain-specific foundation models.