AI Engineering Manager
doolaSalary not specified
- Full Time
- Junior (1 to 2 years)
Not SpecifiedCompensation
Senior (5 to 8 years), Mid-level (3 to 4 years)Experience Level
Full TimeJob Type
UnknownVisa
AI & Machine Learning, Consumer SoftwareIndustries
Requirements
Candidates should have a strong background in machine learning and engineering, with experience in managing engineering teams. A proven track record in deploying machine learning models in production environments is essential. Familiarity with large language models (LLMs) and experience in developing infrastructure for model development are required.
Responsibilities
The Sr. Engineering Manager will lead a team of engineers to enhance AKASA’s machine learning capabilities and deliver innovative products. They will supervise and contribute to all aspects of the LLM stack, including model fine-tuning, inference, evaluation, and deployment. Additionally, they will develop infrastructure and tooling to streamline the model development lifecycle.
Skills
Model fine-tuning
Inference
Evaluation
Deployment
LLM stack
Infrastructure
Tooling
AKASA
Automates revenue cycle management processes
About AKASA
AKASA offers technology solutions that enhance efficiency and revenue outcomes for healthcare organizations by using a mix of computer vision-based Robotic Process Automation (RPA), machine learning, and human-in-the-loop automation. Their services streamline complex Revenue Cycle Management (RCM) workflows, allowing healthcare staff to focus on more meaningful tasks. Unlike competitors, AKASA specifically targets operational inefficiencies in revenue cycle departments, helping clients like Methodist Health System achieve significant gross yield increases. The company's goal is to empower healthcare organizations to improve their operational efficiency and patient experience.
San Francisco, CaliforniaHeadquarters
2018Year Founded
$199.4MTotal Funding
SERIES_CCompany Stage
AI & Machine Learning, HealthcareIndustries
51-200Employees