Ever (evercars.com)
Software Engineer
San Francisco, CA · Staff+ · Full-time
Sponsorship not specified$140k-$300kDetected 160 days ago
TypeScriptPythonGoReactNext.jsViteFull-Stack DevelopmentLeadership
About the role
- Our AI-native product and operations power our full-stack auto retail business, serving EV buyers and sellers across the nation, both online and offline.
- After our recently completed Series A financing, we have raised $100M in total equity and debt funding.
- We encourage applicants from all backgrounds and experiences to apply.
Responsibilities
- Build and ship core product features across Ever's marketplace and operations platform
- Partner closely with Product, Design, Data, and Operations teams to deliver high-impact solutions
- Participate in technical design reviews, architecture decisions, and interviewing
- 3-8+ years of building full stack applications
- Passion for building real-world products with visible impact
- Ever is building the future of auto retail.
- As the first AI-native auto retail platform, we are building the next $100B+ automotive business, starting with electric vehicles.
- We are growing rapidly and actively recruiting exceptional talent to join our mission of building the next-generation auto retail platform.
- Build AI-native core technology for the fastest-growing EV retailer in the U.S.
- Collaborate directly with founders and senior leadership
Requirements
- Experience with Go and/or TypeScript, Python, and related frameworks (React, Next.js, Vite, etc.)
Compensation
- $140,000-$300,000
- Competitive salary and equity options
Benefits
- Competitive salary and equity options
- Health, dental, and vision
- Flexible / Unlimited PTO
Company info
- Work on an AI-native platform with real customers and revenue
Equal opportunity
- Ever is an equal opportunity employer.
Apply directly at Ever (evercars.com) →Create a free account for alerts like thisView Ever (evercars.com) immigration profile
This listing is sourced directly from Ever (evercars.com)'s careers page and normalized into a canonical job model.