RemoteWorker US

Machine Learning Engineer (Remote)

Job description

Location: Remote-North America (EST preferred)

KAR Global is looking for a new Machine Learning Engineer (Remote) to join our team of nimble Data Ninjas! This position will encompass all aspects of the machine learning deployment including training and serving pipelines, data cleansing ETL pipelines, model deployment and managing changes to critical real-time ML applications. An ideal candidate must have deep hands-on experience in implementing ML models in production and the ability to own the entire training and serving architecture.

Who We Are:

KAR Global is an online marketplace for auto dealers and is changing the way they buy/sell wholesale vehicles. Through KAR Global, auto dealers, auto finance, and rental car companies can transact without having to go to physical auctions. Our end-to-end solution is truly seamless for dealers, as KAR Global provides vehicle inspections, transportation, and inventory finance. Our mission is to make wholesale easy so dealers can be more successful. The team at KAR Global includes professionals from both inside and outside the auto industry, which gives us a unique perspective on its problems and innovative solutions. We blend our deep expertise in the auto industry and technology to modernize auto wholesale. We were founded in Kansas City and graduated from 500 Startups, a prestigious accelerator in Silicon Valley. Since launching, we’ve seen tremendous growth and now help dealers buy and sell thousands of cars each month all across the United States. We are looking for individuals who share our growth mindset, and who are ready to execute ideas that will change an industry. If you are excited about marketplace growth and next-generation technology, we want to hear from you.

Responsibilities and Duties:
  • Responsible for architecting, implementing and owning the model serving eco system including model deployment process and training and serving pipelines.
  • Serve as a critical member of the Data Science team and provide inputs from model inception to model deployment who educates & drives the best practices in ML Engineering.
  • Build and own continuous delivery ETL and analytics systems and content that are: almost real time, highly reliable, adaptable and reusable.
  • Collaborate with cross-functional teams for information sharing and on-going process improvement endeavors.
  • Develop and implement data collection systems and other strategies that optimize statistical efficiency and data quality.
  • Research and implement the best practices in context to ML DevOps, pipelines, API, implementation and system integrity checks.

Requirements and Qualifications:
  • Minimum 2 years of professional experience as a Machine Learning Engineer in a large-scale ML environment.
  • Software Engineering background preferred
  • Experience with statistical and ML tools based on Python including TensorFlow or PyTorch
  • High proficiency in SQL based query languages. (Multiple flavors preferred)
  • Basic understanding of No-SQL based systems
  • In depth programming experience in Python and hands on experience in OOPS.
  • Experience with data visualization tools: Domo (preferred) or similar (PowerBI, Tableau)
  • SnowFlake experience.
  • Strong experience in AWS stack, containers and Git.
  • Software engineering mindset with experience in various kind of testing, release plan etc.
  • Create templates and understanding of API and usage, ML pipeline creation and maintenance.
  • Setting up triggers and provisioning resources using Terraform and ADO pipelines
  • Deploying models and inference endpoints through containers and CICD pipelines
  • Using tools that facilitate experimentation with ML models
  • Building of data pipelines and preprocessing, data versioning, etc.
  • Be good at implementing best logging practices and hooking them up to dashboards
  • Adept at using large data sets to find opportunities for product and process optimization and using models to test the effectiveness of different courses of action.
  • Must have strong experience using a variety of data mining/data analysis methods, using a variety of data tools, building and implementing models, using/creating algorithms and creating/running simulations. We prefer python as the main programming language.
  • Develop company a/b testing framework and test model quality.
  • Coordinate with different functional teams to implement models and monitor outcomes.
  • Develop processes and tools to monitor and analyze model performance and data accuracy.

We thank all applicants for their interest. Only candidates selected for an interview will be considered.

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