Machine Learning Engineer

Air Inc
Pittsburgh, PA, United States
10 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Experience level
Expert
Experience required
3 years minimum
Working hours
Regular working hours

Tech stack

Training Data Artificial Intelligence Data Analysis Software as a Service Data Structures Design of User Interfaces Human-Computer Interaction Python (Programming Language) Machine Learning Language Modeling Natural Language Processing Open Source Technology
+10 more
Software Tools Software Engineering SQL Databases Supervised Learning Scripting Large Language Models Kubernetes Data Analytics Machine Learning Operations Multiaccess Edge Computing

Job description

We are seeking a Natural Language Processing expert to join our team and help us build cutting-edge machine learning technology that will replace complex, time-consuming, manual processes with automation and intelligence that helps Air end-users make scientific and analytical decisions. We’re looking for world-class talent to join our team where you will have the opportunity to help develop a wide range of solutions that transform natural language data into useful features for classification algorithms, as well as implement the latest technology to improve user search functionality. In order to do this job well, you must be a curious and eager problem solver with a hunger for building well-designed models and solving problems in an ambiguous space. You share our intolerance of mediocrity. You’re uber-smart, challenged by figuring things out and producing simple solutions to complex problems. Knowing there are always multiple answers to a problem, you know how to engage in a constructive dialogue to find the best path forward. You’re scrappy. We like scrappy. This role is a full-time position located out of our office in Pittsburgh, PA. This role may require up to 10% travel Scope of Responsibilities

  • Inform and implement the design and development of NLP applications to enhance the intelligence and efficiency of our data analytics software-as-a-service platform
  • Review, verify, and aggregate the most appropriate annotated datasets for the best-supervised learning methods
  • Ability to work with taxonomy experts to create and validate dataset annotation
  • Use well-formed and effective text representation to change and adapt natural language documents into user-friendly features
  • implement the latest technology to improve user search functionality as well as integrate state of the art LLM models into our Air Enterprise Readiness platform to enhance user experience.
  • Develop new algorithms and modeling techniques and conduct sound experiments to verify model results and integrate models into the live production system
  • Establish meaningful criteria for evaluating algorithm performance and suitability
  • Implement working, scalable, production-ready models and code
  • Keep up to date with Machine Learning best practices and evolving open-source frameworks
  • Regularly seek out innovation and continuous improvement, finding efficiency in all assigned tasks
  • Collaborate closely with fellow taxonomists, software engineers, data scientists, data engineers, and QA engineers

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, * 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
  • Practical experience in building, developing, and productionizing both supervised and unsupervised machine learning models
  • Advanced software skills in Python
  • Advanced ability in forming SQL queries
  • A strong desire to learn, investigate, and implement cutting-edge technologies
  • Ability to work collaboratively throughout the design process.

Desired Skills:

  • 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 developing custom training sets for large language models
  • Experience productionizing transformer-based models

We firmly believe that past performance is the best indicator of future performance. If you thrive while building solutions to complex problems, are a self-starter, and are passionate about making an impact in global security, we’re eager to hear from you. Air is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans status or any other characteristic protected by law.

Skills: Algorithms, Analysis Skills, Analysis Software, Artificial Intelligence (AI), Automation, Best Practices, Continuous Improvement, Data Analysis, Data Modeling, Data Science, Data Structures, Emerging Technology, Entrepreneurship, Government, Leading Edge Technology, Machine Learning, Modeling Languages, Natural Language Processing (NLP), Open Source, Problem Solving Skills, Production Systems, Python Programming/Scripting Language, Quality Assurance, SQL (Structured Query Language), Security Clearance, Software Design, Software Development, Software Engineering, Software as a Service (SaaS), Startup, Taxonomies, Team Player, Training Data Sets, United States Citizen, User Interface/Experience (UI/UX), Willing to Travel

About the company

Air is the leader in Enterprise Readiness. Our mission is to establish readiness as a real-time condition that is continuously achieved. Today, a dangerous Readiness Gap exists between what the front line needs and what is delivered. Our AI-native platform, Air Enterprise Readiness, aligns development, production, delivery, and sustainment into one coordinated execution system for government agencies and industrial suppliers. 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.

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