AKASA is on a mission to build the future of healthcare with AI, focusing on generative AI solutions for the healthcare revenue cycle.
The company has raised over $205M in funding from notable investors and has experienced significant growth in bookings for its new product suite.
The customer base includes innovative health systems such as Stanford, Johns Hopkins, and Cleveland Clinic, representing over $120B in net patient revenue.
The role of Sr. Machine Learning Engineer is remote, reporting to the Engineering Manager, and involves collaboration with PhD Researchers, ML Engineers, and healthcare experts.
Responsibilities include developing state-of-the-art ML solutions for large-scale healthcare problems, creating data pipelines, writing production-ready software, and developing ML algorithms for various applications.
The position requires ownership of ML services from problem discovery to service deployment, and the development of novel, application-specific ML models.
Requirements:
A Master's degree in Computer Science or a similar field is required.
Candidates must have 5+ years of work experience in machine learning and data engineering.
Experience in launching production systems from the ground up is necessary.
Proficiency in programming languages such as Python and C++ is required.
Development experience with cloud platforms like Spark, AWS, and Kubernetes is essential.
Knowledge of ML frameworks such as Scikit-Learn, Pytorch, and TensorFlow is needed.
Full-stack development experience for an end-to-end ML solution is preferred.
Ideal candidates should have experience with Large Language Models, including open-source model deployment and agentic workflows.
Benefits:
The position offers unlimited paid time off (PTO).
Employees receive expansive coverage for health, dental, and vision insurance.
There is an employer contribution to Health Savings Accounts (HSA).
A generous parental leave policy is provided.
Full employee coverage for life insurance is included.
The company offers paid holidays and a 401(K) plan.