Base Salary
$175k - $230k/yr
Responsibilities
- Develop state-of-the-art machine learning solutions for large-scale healthcare problems
- Own ML services end-to-end, including problem discovery, data pipeline development, inference optimization, model experimentation, and service deployment
- Build pipelines that collect, preprocess, and deliver measurable-quality data
- Write production-ready software with well-tested and efficient algorithms
- Develop ML algorithms involving large language models, probabilistic inference, and computer vision
- Build application-specific ML models for medical document entity extraction, medical coding, and claim outcome prediction
Requirements
- Master’s degree in Computer Science or a similar field
- 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 open-source model deployment and agentic workflows
- Experience directly training or fine-tuning LLMs is ideal
- Proficiency in one or more programming languages such as Python and C++
- Development experience with Spark, AWS, and Kubernetes
- Knowledge of ML frameworks such as Scikit-Learn, PyTorch, and TensorFlow
Benefits
- Flexible paid time off (PTO)
- Health, dental, and vision coverage
- Employer contribution to Health Savings Accounts (HSA)
- Generous parental leave
- Employer-paid life insurance
- Home office stipend
- Cell phone and internet reimbursement
- Company-paid holidays
- 401(k) plan
- Remote work is supported, with local R&D teams expected in the South San Francisco office every Wednesday for co-working days
Tech Stack
Categories
About AKASA
AKASA is rapidly becoming the GenAI partner of choice for leading health systems, with recent national recognition of our Cleveland Clinic deployment as “one of the most comprehensive real-world uses of GenAI in healthcare finance to date.” Across customers, AKASA now delivers an average of $3.1M in additional revenue and 4,270 additional quality indicators per 10,000 discharges, demonstrating consistent, measurable impact. Our advantage comes from building a custom LLM for each health system, capturing their clinical nuance to drive superior accuracy across the mid-cycle — spanning prior authorization, CDI, inpatient coding, and clinical denials appeals. This approach allows organizations to modernize critical workflows and unlock material improvements in both revenue and quality. With investors including Andreessen Horowitz, BOND, Costanoa Ventures, and others, we’ve raised more than $200M to help eliminate the financial complexity of healthcare. We’re rebuilding the revenue cycle with AI. Let’s chat.