491 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:" positions at University of California, Los Angeles in United States
Sort by
Refine Your Search
-
Listed
-
Category
-
Field
-
JOB DUTIES Description The Machine Learning Specialist plays a key role in advancing UCLA Health’s AI and machine learning capabilities. This position contributes to the development, evaluation
-
, (AI), Machine Learning (ML), etc. The team is comprised of Application Developer, Architects, and Engineers. The team is responsible for the core foundational Cloud platforms and Evaluating technology
-
foundations ML Engineering & MLOps Feature engineering and feature store development CI/CD for machine learning workflows Monitoring, maintenance, and retraining of production ML models Collaboration with data
-
and ML development Cloud & Data Platforms Experience or interest in Azure and Databricks for analytics and ML workloads Machine Learning & MLOps Concepts Feature engineering, feature stores, CI/CD
-
programming Expertise in workflow optimization Training in machine learning and AI Highly organized, efficient, and attentive to detail Analytical, resourceful, and able to work independently Strong
-
QUALIFICATIONS Qualifications Required: California State Speech Pathology license Excellent communication and interpersonal abilities Able to efficiently learn new computer and clinic systems Applicant should have
-
for data engineering and ML development · Cloud & Data Platforms o Experience or interest in Azure and Databricks for analytics and ML workloads · Machine Learning & MLOps Concepts o Feature
-
notebook and organize common lab resources. Knowledge of basic cell biology, molecular biology, and biochemical principles. Ability to use laboratory equipment, including centrifuges, pH meter, PCR machines
-
learn new computer and clinic systems Excellent documentation abilities At least one year of experience (no CFY)
-
notebook Experience with regular expression natural language processing via Python Experience with machine learning models (e.g. regularization regression, random forests, ensemble methods) Experience with