Machine Learning Engineer
Pattern AI
Apply to this jobPatternAI is an automated machine learning platform that reveals critical patterns in data for narrow business problems.
We’re seeking an outstanding ML Engineer to join our data team and help build out best-in-class machine learning solutions on our platform, powering innovative solutions in marketing & sales and commercial analytics.
Responsibilities:
Build and deploy the ML pipelines that power PatternAI’s machine learning platform.
Manage MLOps infrastructure to monitor and optimize models.
Experience:
3+ years of professional experience as a Machine Learning Engineer or production-focused Data Scientist.
Proficiency across topics in machine learning and statistics.
Fluency in Python coding as well as data manipulation (SQL, Spark, Pandas)
Broad familiarity with the Python ecosystem and common libraries including Scikit-Learn, XGBoost, PyTorch, Keras, Tensorflow, Pandas, and common ML cloud services.
Familiarity with CNNs, RNN, LSTMs, and the latest research trends.
Experience implementing, deploying, and maintaining production machine learning systems.
Experience monitoring and optimizing model performance.
Experience with Linux, Docker and AWS, and basic development operations.
Advanced degree in computer science, mathematics, statistics or related area of study strongly preferred.
About PatternAI
PatternAI is an early stage startup that is growing rapidly and recently closed a successful round of venture funding. We are emerging from stealth and with an exciting series of machine learning products and a rapidly growing number of enterprise customers.
All your information will be kept confidential according to EEO guidelines.
Summary
Build and deploy machine learning pipelines, manage infrastructure, optimize models, and monitor performance.
Job title
Machine Learning Engineer
Experience level
3+ years
Industry
software
Location requirements
San Mateo, CA, remote work allowed
Salary
Not specified
Management role
No
Required skills
Preferred skills
Specializations
Structured locations inferred from the posting.
San Mateo, CA, USA