Senior Full-Stack Engineer – Real-Time Data & AI UI

Nodi; New York (NY); Hybrid; USD 175,000 - 225,000; Full time

Job summary

Nodi, a US-based materials discovery company, is seeking a mid-level to senior Full Stack Engineer to build frontends and data pipelines for real-time scientific visualization. You will work with a Go backend, Protobuf schemas, and MongoDB data models, shipping features end-to-end while collaborating with product managers and designers.

The role emphasizes frontend delivery with AI agent integration, high-quality UI, and scalable state management across large datasets.

Qualifications

• Proficient in TypeScript, HTML and CSS with responsive design experience.
• Strong frontend framework experience, especially Svelte/SvelteKit; React/Vue acceptable if learning Svelte.
• Familiarity with modern tooling and high-performance frontend architecture.
• Experience with data visualization for scientific charts and real-time UI updates.

Responsibilities

• Build and maintain data-rich web apps using Svelte and SvelteKit.
• Implement UI from designs and develop data visualizations for research.
• Extend Cortex Go backend with RPC endpoints and schemas; keep types in TS/Go/Python in sync.
• Own features end-to-end across API, service, and UI; collaborate with PMs and designers.
• Write tests and contribute to CI/CD pipelines.

Skills

TypeScript

HTML

CSS

Svelte/SvelteKit

React/Vue (alt)

Tailwind (optional)

Build tooling

Data visualization

AI integration

Tools

Go

MongoDB

Protobuf

gRPC

SvelteKit tooling

Vite

Node.js

Job description

Nodi, a US-based materials discovery company, is seeking a mid-level to senior Full Stack Engineer to build frontends and data pipelines for real-time scientific visualization. You will work with a Go backend, Protobuf schemas, and MongoDB data models, shipping features end-to-end while collaborating with product managers and designers.

The role emphasizes frontend delivery with AI agent integration, high-quality UI, and scalable state management across large datasets.