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Staff Machine Learning Engineer
Posted on 3/8/2022
INACTIVE
Locations
Alameda, CA, USA
Experience Level
Entry
Junior
Mid
Senior
Expert
Desired Skills
Apache Spark
BigQuery
Google Cloud Platform
Kafka
Management
Pytorch
Scala
Splunk
Tensorflow
Kubernetes
Scikit-Learn
Apache Thrift
gRPC
Requirements
  • Bachelor's degree (or foreign equivalent) in Computer Science, Engineering, Computer Information Systems, Mathematics, Physics, or a related field & 5+ years of experience involving: application of cloud technologies, infrastructure, and application of distributed systems
  • Deep knowledge of personalization and recommendations systems
  • Building and operating machine learning platform
Responsibilities
  • As a Machine Learning Engineer, you'll be a member of a team of Machine Learning Engineers and Data Engineers working closely with Data Scientists
  • You'll be working on the machine learning infrastructure and recommendation systems to help data scientists develop and run models which will be used to improve user experience through better personalization
  • You'll build scalable platforms for machine learning, prediction serving, and personalized recommendation serving
  • The machine learning platform provides tools and services for building feature transformations, distributed deep learning models, model lifecycle management, counterfactual simulation and budget optimization for continuously improving user engagement and revenue generation
  • The machine learning platform provides capabilities for feature engineering, monitoring feature quality and serving features at scale for the models
  • You will be using a variety of technologies such as Apache Beam, Apache Spark, Tensorflow, Scikit-Learn, TF-Serving and Apache Airflow on large amounts of data
  • Technologies for production serving and personalization also include Scala, GCP, BigTable, BigQuery, GRPC, Thrift, GKE, Splunk and Grafana
Desired Qualifications
  • Master's degree in Computer Science, Engineering, Computer Information Systems, Mathematics, Physics, or a related field
  • Modern data technologies include technologies such as in-memory, distributed and serverless processing (e.g. spark/dataflow/cloud-functions), key/value vs. document vs. column oriented datastores (e.g. bigquery, bigtable, cloud-search), message queues (kafka/pub-sub), elastic scaling (kubernetes, instance-groups), etc
  • Modern machine learning and optimization technologies include familiarity with toolkits like sklearn, scipy, tensorflow and pytorch
Credit Karma
Company mission
Credit Karma offers a range of tools and personalized recommendations designed to help customers make the most of their money.