PatternAI, Inc.

PatternAI, Inc.

Machine Learning Engineer

San Mateo, CA, United States

Sponsorship not specifiedDetected 1608 days ago
PythonSQLAWSDockerLinuxMachine LearningTensorFlowPyTorchscikit-learnPandasSparkMLOpsStatisticsResearch

About the role

  • About PatternAI PatternAI is an early stage startup that is growing rapidly and recently closed a successful round of venture funding.
  • All your information will be kept confidential according to EEO guidelines.

Responsibilities

  • Manage MLOps infrastructure to monitor and optimize models.

Nice to have

  • 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 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.

Benefits

  • Build and deploy the ML pipelines that power PatternAI's machine learning platform.
  • 3+ years of professional experience as a Machine Learning Engineer or production-focused Data Scientist.
  • Proficiency across topics in machine learning and statistics.
  • Experience implementing, deploying, and maintaining production machine learning systems.
  • We are emerging from stealth and with an exciting series of machine learning products and a rapidly growing number of enterprise customers.
  • PatternAI 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.

This listing is sourced directly from PatternAI, Inc.'s careers page and normalized into a canonical job model.