About Ema

Ema is building the world’s leading Agentic AI platform to transform enterprise productivity. We enable organizations to delegate repetitive tasks to Ema, the Universal AI Employee, delivering 10x gains in workforce efficiency, across functions. 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, Section32, and angels like Sheryl Sandberg and Dustin Moskovitz. Headquartered in Silicon Valley and with offices in London, Bangalore and Vancouver and Bangalore, Ema is at the frontier of what Agentic AI can do in production — we ship real systems that run real business processes at scale.

At Ema, we build AI Employees that operate inside the enterprise. Healthcare is where the bar is highest: the output has to be accurate, auditable, and clinically sound.

We're opening a part-time role focused on agent measurement and improvement. You'll instrument how our healthcare AI Employees perform in production, identify where quality breaks down, and design the experiments that close the gap — partnering with clinical experts to ensure improvements translate into better patient outcomes, not just better metrics.

We're looking for strong analytical judgment (Python, agent lead development), comfort operating with incomplete information, and genuine interest in the healthcare domain. Clinical experience is valued but not required.

This engagement is structured as a paid internship or contract engagement, with weekly syncs in our Bay Area office.

Compensation offered will be determined by factors such as location, level, job-related knowledge, skills, and experience. Certain roles may be eligible for variable compensation, equity, and benefits.

Ema Unlimited is an equal opportunity employer and is committed to providing equal employment opportunities to all employees and applicants for employment without regard to race, color, religion, sex, national origin, age, disability, sexual orientation, gender identity, or genetics.