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Machine Learning Engineer
Confirmed live in the last 24 hours
Locations
Austin, TX, USA
Experience Level
Entry
Junior
Mid
Senior
Expert
Desired Skills
Agile
Data Science
Development Operations (DevOps)
SQL
Python
Requirements
  • 2+ years of experience in a data engineering, software engineering, DevOps, or similar role
  • 2+ years of experience developing in Python and SQL
  • 1+ years of experience working on or supporting data science projects
  • Understanding of data science terminology and the ability to communicate with Data Scientists on highly technical machine learning projects
  • Understanding of the machine learning lifecycle development and deployment process
  • Experience managing infrastructure of machine learning platforms or systems running operational machine learning models
  • Understanding of common data architectures, processes, and paradigms such as data warehousing/modeling, ETL/ELT, feature engineering, batch vs streaming pipelines
  • Ability to articulate, diagram, and document technical data science and engineering concepts
  • Experience with industry-standard Data and Machine Learning Platform technologies such as data warehouses, workflow orchestration platforms, database replication platforms, data quality frameworks, feature stores, data science notebooking tools, etc
  • Experience or familiarity working in agile frameworks
Responsibilities
  • Work with a team of Machine Learning Engineers to execute on roadmap items and strategic initiatives for Machine Learning Platform components and capabilities
  • Deploy and monitor infrastructure supporting our machine learning model deployments and development environments
  • Build batch and real-time pipelines for feature engineering and understand when to use each implementation
  • Facilitate machine learning model deployments through different environments and coordinate dependencies across Data Science, Data Engineering, and Product Engineering teams
  • Implement and document team standards and machine learning operations (MLOps) best practices that have a broad impact on Data and Engineering teams
  • Execute on strategies for reducing the gap between building models in a development environment and running them in production
  • Implement monitoring and alerting frameworks to ensure data quality in our feature engineering pipelines and machine learning deployments
  • Develop Python libraries, functions, and other shared components that enable easier sharing of resources across many Data Science teammates and machine learning models
Arrive Logistics

1,001-5,000 employees

Carrier and customer-centric logistics company
Company Overview
Arrive Logistics is defining a new standard for experience in freight, pushing the limits of what's possible for themselves and their partners every day.
Benefits
  • Uncapped earning potential
  • Matching 401(k)
  • World class training program
  • Nationally recognized company culture
  • Incredible sales contests