Air

Air

Senior Machine Learning Engineer

Pittsburgh, Pennsylvania, United States · Senior · Full-time

No sponsorshipDetected 11 days ago
PythonData StructuresAlgorithmsSQLKubernetesMachine LearningData AnalysisData ScienceNLPLLMsCybersecurity

About the role

  • Company Description Air is the leader in Enterprise Readiness.
  • Today, a dangerous Readiness Gap exists between what the front line needs and what is delivered.
  • By revealing true capacity, exposing real constraints, coordinating resources, and executing at the speed of operational demands, the front line gets what it needs to succeed.

Requirements

  • U.S. Citizenship is required
  • Advanced degree, or bachelor's with at least 3 years of experience, in Data Science, Machine Learning or a related field

Skills

  • Minimum 3 years experience with hands-on development of NLP models
  • In-depth understanding of NLP methods for text representation, semantic extraction techniques, data structures, and modeling
  • Advanced software skills in Python
  • Advanced ability in forming SQL queries
  • Ability to work collaboratively throughout the design process.
  • Current possession of a U.S. security clearance, or the ability to obtain one with our sponsorship
  • Experience in or exposure to the nuances of a startup or other entrepreneurial environment
  • Experience in deploying ML models in Kubernetes environments
  • Experience productionizing transformer-based models
  • Air is an Equal Opportunity Employer.

Company info

  • Company Description
  • Air is the leader in Enterprise Readiness.
  • Our mission is to establish readiness as a real-time condition that is continuously achieved.
  • Our AI-native platform, Air Enterprise Readiness, aligns development, production, delivery, and sustainment into one coordinated execution system for government agencies and industrial suppliers.

Visa & Work Authorization

  • Current possession of a U.S. security clearance, or the ability to obtain one with our sponsorship

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