Part-Time Full-Stack Engineer: End-to-End Data Systems

Moon; Palo Alto (CA); On-site; USD 151,536 - 220,416; Part time

Job summary

Moon is seeking a curious Full-Stack Engineer for an in-office, part-time role (24-28 hrs/week) at an hourly rate of $110–$160, with the expectation of growing into a full-time position as we scale.

You will own development and testing across our web applications and data-capture infrastructure, collaborating with cross-disciplinary design and technical teams to define feature priorities, design scalable solutions, and ship fast while learning from usage feedback.

Qualifications

• Full-stack proficiency with modern frontend (React, TypeScript) and robust backend (Node.js, Python) stacks; experience with containerization and CI/CD pipelines.
• Able to contribute to product direction, design, and customer-facing experience, not just ticket closure.
• Comfort balancing fast iteration with high-quality engineering across opinionated infrastructure in a growth environment.

Responsibilities

• Data Operations: Expanding telemetry and data ingestion pipelines for analysis.
• Cross-system Reliability: Increasing robustness and coverage of end-to-end tests, CI, and reliability checks across multiple hardware modalities.
• Core Feature Buildout: Building frontend and backend interfaces that enable internal and external users to interact with the system.
• Core Infrastructure: Working on data models and interfaces that drive systems from the web to the machine tool.

Skills

React

TypeScript

Node.js

Python

CI/CD pipelines

Containerization

Cloud-native architectures

Product intuition

Systems fluency

Tools

Docker

Job description

Moon is seeking a curious Full-Stack Engineer for an in-office, part-time role (24-28 hrs/week) at an hourly rate of $110–$160, with the expectation of growing into a full-time position as we scale.

You will own development and testing across our web applications and data-capture infrastructure, collaborating with cross-disciplinary design and technical teams to define feature priorities, design scalable solutions, and ship fast while learning from usage feedback.