Design Engineer
Doubling · health and wellness
midRemote
Salary Range (USD)
Negotiable
Location
Chicago, USA
Visa Support
Not Supported
Funding Stage
Unknown
Job Responsibilities
- • own the mobile UX end-to-end
- • product design
- • motion / interaction design
- • code contributions
Required Skills
2+ years combined in product design and motion / interaction designat least one shipped mobile productportfolio that demonstrates both UI and motion workcomfort in a frontend codebase
Engineering Culture & Tech Stack
FigmaReact NativeRiveLottieAfter EffectsPrincipleFramer
product-minded
customer-focused
Raw Post
Show original text
Doubling | Design Engineer | Chicago or Bay Area preferred | REMOTE (US) also considered | CONTRACT (1099), 10-20 hrs/week
Doubling is building a new mobile product in the health and wellness space. Small founding team, mission-driven, 0-to-1 stage.
We're looking for a Design Engineer to own the mobile UX end-to-end: product design, motion / interaction design, and code contributions where it helps the team move faster. The core of the role is product and motion design; code contributions are valued but not the primary skill we're hiring for.
Stack / tools: Figma, React Native, plus at least one motion or prototyping tool (Rive, Lottie, After Effects, Principle, or Framer).
What we want: 2+ years combined in product design and motion / interaction design, at least one shipped mobile product, a portfolio that demonstrates both UI and motion work, and enough comfort in a frontend codebase to land small UI tweaks and animations (or genuine interest in growing into that).
Bonus: prior consumer health, wellness, fitness, or nutrition product experience; design system ownership; founding / first designer at a startup.
Must be authorized to work in the U.S. as an independent contractor. No visa sponsorship for this role.
Details: https://www.doubling.io/careers/design-engineer
Apply: careers@doubling.io
AI Risk Insights
No major risk signals detected.
Recent News
No recent updates
Data Source
Content parsed by LLM from Hacker News raw data. Confidence:HIGH