Sr. Machine Learning Engineer

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 most recent recognitions include being named the #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 five years in a row, just to name a few.

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 remote 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 both 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.

AKASA is based in South San Francisco. As a company, we embraced remote work and consider ourselves experts in working collaboratively wherever our team members happen to reside.

What You’ll Do

• Participate in developing state-of-the-art ML solutions to address large-scale healthcare problems
• 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 computer vision, large-language models, and probabilistic inference to solve problems like medical document entity extraction, medical coding and claim outcome prediction
• Own ML services end-to-end, including problem discovery, data pipeline development, inference optimizations, model experimentation, and service deployment
• 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
• Have experience launching production systems from the ground up
• 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
• Full-stack development experience for an end-to-end ML solution
• Ideal experience with Large Language Models, including both open source model deployment and agentic workflows.

What We Offer

• Unlimited 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
• Company-paid holidays401(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.