Phaselaw
Member of Technical Staff, Frontend
San Francisco · Staff+
Sponsorship not specifiedDetected 178 days ago
TypeScriptReactNext.jsHTMLCSSTailwind CSSDistributed SystemsSnowflakeMachine LearningA/B TestingFigmaRoboticsResearchCommunicationCollaboration
About the role
- You'll work closely with our founding designer and backend / ML teams to ship new product surfaces, workflows, and interactions at startup speed.
- You'll be responsible for making sure everything looks right, feels right, and works flawlessly, from complex video review dashboards to high-impact landing pages.
- The ideal candidate is deeply execution-oriented, comfortable using modern AI-powered dev tools to accelerate development, and excited to experiment, iterate, and ship.
Responsibilities
- Bring Designs to Life
- Implement high-fidelity product and marketing designs with precision. Translate Figma files into responsive, accessible, and performant web experiences.
- Build and own major parts of NomadicML's web application, including dashboards for video review, search, edge-case exploration, and customer workflows.
- Design-Engineering Collaboration
Nice to have
- Experience with data-heavy or visualization-rich interfaces
- Familiarity with Tailwind, Framer Motion, or similar UI/animation libraries
- Interest in AI, robotics, autonomous vehicles, or other deep-tech domains
- Startup or early-stage company experience
- You'll have real ownership, real influence, and the freedom to move fast.
- We value craft, speed, and judgment.
- If you want to ship ambitious frontend work at the edge of AI and physical systems, and do it fast, we'd love to talk.
- Deep experience with React and modern frontend frameworks (Next.js preferred)
Skills
- Experience using AI tools to accelerate development and experimentation
- Clear communication skills and a bias toward action
Apply directly at Phaselaw →Create a free account for alerts like thisView Phaselaw immigration profile
This listing is sourced directly from Phaselaw's careers page and normalized into a canonical job model.