5 months ago
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 builds generative AI software that automates hospital revenue cycle and mid-cycle workflows — including prior authorization, clinical documentation integrity, inpatient coding, and denials appeals — for U.S. health systems. Its platform uses custom large language models tailored to each customer and is sold as enterprise software and services. Founded in 2019 and headquartered in South San Francisco, the company is privately held and raised a Series C in 2022.