Software Engineer, Applied AI

🏢 Akasa · 📍 San Francisco · 💼 hybrid · ⏰ FullTime

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Position Summary & Responsibilities

About AKASA

AKASA is building the future of healthcare by developing AI-native solutions for some of healthcare’s most complex, consequential work. Our customers include the most innovative health systems in the country, like Cleveland Clinic, Mayo Clinic, NewYork-Presbyterian, Duke, Stanford, and Johns Hopkins. Our customer base represents 1 in 10 inpatient discharges across the United States and more than $180B+ in net patient revenue.

Our products help health systems comprehensively capture and communicate the full patient clinical journey. By empowering health systems to streamline their operations, they can focus on what matters most - delivering quality patient care.

We train internal models and build domain-specific harnesses to power our products and best capture clinical nuance for each customer. We hire people with high agency and good judgment. Everyone here is an owner and builder who is looking to help solve high stakes challenges every day.

We have raised over $250M in funding from investors such as Andreessen Horowitz, BOND, and Coatue. Our deployments have been recognized nationally as "one of the most comprehensive real-world uses of GenAI in healthcare finance to date" (link).

Some of our recent recognitions include being named one of America's Top Startup Employers 2026 by Forbes, a Top Healthcare Technology Company of 2026 by Healthcare Technology Report, the #1 most promising healthcare RCM startup of 2025 by Black Book Market Research, and we have been certified as a “Great Place to Work” for the past 6 years in a row.

About the Role

As an Applied AI Engineer, you will be building AI-powered solutions across our Clinical Documentation and Medical Coding suite. You will work closely with the product, design, and machine learning teams to

🎯 Candidate Preparation Guide For Akasa Applicants

Key Role Competencies & Evaluation Highlights

Key Role Focus:

Evaluating healthcare revenue cycle automation expertise, secure LLM/NLP orchestration for unstructured medical records, and enterprise-grade system scalability.

📋 Preparation Priorities (Key Areas to Clarify):

1. Displaying ignorance regarding healthcare compliance, billing workflows, or payer denials.
2. Proposing black-box AI solutions without auditable decision trails for healthcare auditing.
3. Underestimating the complexity of integrating with legacy hospital EMR/EHR systems like Epic or Cerner.

✨ Recommended Role Alignment Hook:

I automate complex healthcare revenue cycles by combining secure, auditable AI agents with deep domain knowledge of medical billing.

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