Ema Unlimited
Software Engineer, Full-Stack
Mountain View (Hybrid) · Mid
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odds of building a lasting career here
Sponsors, but it's cap-subject — you still face the weighted lottery (~61% per draw at Level IV). Good if you win; have a cap-exempt backup on your list.
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About the role
- Headquartered in Silicon Valley and with offices in London, Bangalore and Vancouver, Ema is at the frontier of what Agentic AI can do in production.
- We ship real systems that run real business processes at scale.
Responsibilities
- Design, develop, and implement exceptional user interfaces for web applications using modern web technologies such as HTML, CSS, and JavaScript.
- Develop and maintain API integrations, ensuring seamless communication between front-end and back-end systems using React and gRPC.
- Build, test, and deploy full-stack web applications using React and gRPC, while ensuring responsiveness, performance, and scalability.
- Optimize web applications for performance, accessibility, and SEO best practices.
- Familiarity with version control systems like Git and build/deploy platforms such as Vercel.
- Ema builds AI Employees for HR, IT and Finance.
Requirements
- Bachelor's degree in Computer Science, Web Development, or a related field, or equivalent work experience.
- Strong proficiency in HTML, CSS, and JavaScript, with extensive experience in React.
- Experience with gRPC for client-server communication, and familiarity with RESTful APIs, JSON, XML, and OAuth.
Compensation
- Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience.
Company info
- Founded by former executives from Google, Coinbase, Flipkart, and Okta, our team includes engineers from premier tech companies and graduates of Stanford, MIT, UC Berkeley, CMU, and IITs.
- We are backed by industry leading investors including Accel, Naspers/Prosus, Creaegis, Section32, and angels like Sheryl Sandberg and Dustin Moskovitz.
This listing is sourced directly from Ema Unlimited's careers page and normalized into a canonical job model.