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Salary
$175k – $230k per year
Location
Remote (United States)
Seniority
Senior · 5+ years exp
Employment
Full-Time
Overview
Company
Impact
Profile match
AKASA provides generative AI (GenAI) solutions for the healthcare revenue cycle, from prior auth to CDI to coding to claims management.

About AKASA

At AKASA, our mission is to build the future of healthcare with AI. As the leading provider of generative AI solutions for the healthcare revenue cycle, we 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 have raised over $205M in funding from investors such as Andreessen Horowitz, BOND, and Costanoa Ventures.

This is the most exciting time to join AKASA. Revenue bookings for our new AI-native product suite have grown over 20x since launching in 2024. In this time, we have broken our record for the largest deal in company history three times consecutively. This growth is driven by the massive improvement we are generating for our customers across clinical quality and documentation accuracy, both top priority areas for health system leaders.

Our deployments have been recognized nationally as "one of the most comprehensive real-world uses of GenAI in healthcare finance to date" (link). Our customer base represents more than $120B+ in net patient revenue and includes the most innovative health systems in the country, like Cleveland Clinic, Duke, Stanford, and Johns Hopkins.

Some of our recent recognitions include being named one of America's Top Startup Employers 2026 by Forbes, #1 most promising healthcare RCM startup of 2025 by Black Book Market Research, and one of the fastest-growing GenAI startups to watch by AIM Research. Our CEO was ranked among the “Top 50 Healthcare Technology CEOs” by the Healthcare Technology Report, and we have been certified as a “Great Place to Work” for the past 6 years in a row.

We’re building on this momentum to redefine what’s possible in healthcare. We’re looking for exceptional people to help us accelerate that reality.

About the Role

As a Sr. Machine Learning Engineer, you'll report to our Engineering Manager and work with a talented team of PhD Researchers, ML Engineers, and healthcare experts. You will put machine learning into practice, so your code directly affects our customers immediately. You’ll work with large proprietary medical and clinical datasets containing structured documents, natural language and images. Our mission is to empower healthcare professionals with tools that amplify their capabilities, making them faster, more comprehensive, and more effective in their roles.

The AKASA office is located in South San Francisco. While we support remote work on a variety of teams, we have a strong Bay Area presence across the company. The local R&D teams come into the office every Wednesday for co-working days, which this role will be expected to attend.

What You'll Do

  • Participate in developing state-of-the-art ML solutions to address large-scale healthcare problems

  • Own ML services end-to-end, including problem discovery, data pipeline development, inference optimizations, model experimentation, and service deployment

  • Help develop pipelines that collect, preprocess, and deliver data with a measurable quality

  • Write production-ready software with well-tested and efficient algorithms

  • Develop state-of-the-art ML algorithms across large-language models, probabilistic inference and computer vision to solve problems like medical document entity extraction, medical coding and claim outcome prediction

  • Help build novel, application-specific ML models - all of our products are built from the ground up with ML at their core, enabling us to deploy our predictions in new and interesting ways

Skills & Qualifications

  • Master's degree in Computer Science or similar

  • 5+ years of work experience in machine learning and data engineering

  • Experience launching production ML systems from the ground up

  • Experience with Large Language Models, including both open source model deployment and agentic workflows; ideal: experience directly training or fine-tuning LLMs

  • Proficiency in one or more programming languages such as Python and C++

  • Development experience with cloud platforms such as Spark, AWS & k8s

  • Knowledge of ML frameworks such as Scikit-Learn, Pytorch & TensorFlow

What We Offer

  • Flexible paid time off (PTO)

  • Expansive coverage for health, dental, and vision

  • Employer contribution to Health Savings Accounts (HSA)

  • Generous parental leave policy

  • Full employee coverage for life insurance

  • Home office stipend

  • Cell phone/internet reimbursement

  • Company-paid holidays

  • 401(K) plan

Compensation

  • Based on market data and other factors, the salary range for this position is $175,000-$230,000 + Equity. However, a salary higher or lower than this range may be appropriate for a candidate whose qualifications differ meaningfully from those listed in the job description.

The above represents the expected salary range for this job requisition. Ultimately, in determining your pay, we'll consider your location, experience, and other job-related factors.

We’re committed to doing the best work of our lives, together. Come see if we're the right team for you.

AKASA is a proud equal opportunity employer and we believe that a diverse and inclusive workforce is an imperative. We welcome people of different backgrounds, genders, races, ethnicities, abilities, sexual orientations, and perspectives, just to name a few. We do not discriminate based upon any protected class and we encourage candidates of all identities and backgrounds to apply. AKASA considers qualified applicants regardless of criminal histories in accordance with the San Francisco Fair Chance Ordinance.

AKASA is committed to providing reasonable accommodations for candidates with disabilities in our recruiting process. If you need any assistance or accommodations due to a disability, please let us know at [email protected].

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