Product Engineer Intern — AI-Driven Data Tools

Mosaic.tech

San Francisco (CA)

Hybrid

USD 112,000 - 134,000

Full time

6 days ago
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Benefits offered by this job

Housing stipend
Health benefits
Hybrid work model
Flexible PTO

Job summary

Hex Technologies is seeking a Product Engineer Intern for this summer in a hybrid San Francisco role. You will work across the stack with mentors from design, PM, and AI engineering to deliver features powering data workflows and exploration.

The ideal candidate is pursuing CS/Engineering with plans to graduate in Dec 2026 or 2027, has full-stack experience (JS/TS/Python), and an eagerness to learn AI-driven product experiences in production systems.

Qualifications

  • Pursuing a degree in Computer Science, Engineering, or a related field with planned graduation in December 2026 or 2027.
  • Experience shipping software projects (academic, personal, or professional).
  • Interest in AI-driven product experiences and production systems.

Responsibilities

  • Build real product features across the stack (React, Typescript, Python).
  • Collaborate with AI engineers to prototype agentic capabilities.
  • Design and improve end-to-end workflows for collaborative editing and data exploration.
  • Address engineering challenges including performance and API design.
  • Participate in the full development lifecycle from ideation to launch.

Skills

Full-stack experience
JavaScript/TypeScript
Python
AI-driven product interest
User-centric mindset
Collaboration

Education

Pursuing CS/Engineering degree; graduating Dec 2026/2027

Tools

React
Typescript
Python

Job description

Hex Technologies is seeking a Product Engineer Intern for this summer in a hybrid San Francisco role. You will work across the stack with mentors from design, PM, and AI engineering to deliver features powering data workflows and exploration.

The ideal candidate is pursuing CS/Engineering with plans to graduate in Dec 2026 or 2027, has full-stack experience (JS/TS/Python), and an eagerness to learn AI-driven product experiences in production systems.

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