AI/ML Engineer Intern

Lockheed Martin•Fort Worth, CO

2d•$53,227 - $70,708•Remote

About The Position

By bringing together people that use their passion for purposeful innovation, at Lockheed Martin we keep people safe and solve the world's most complex challenges. Our people are some of the greatest minds in the industry and truly make Lockheed Martin a great place to work. With our employees as our priority, we provide diverse career opportunities designed to propel development and boost agility. Our flexible schedules, competitive pay, and comprehensive benefits enable our employees to live a healthy, fulfilling life at and outside of work. At Lockheed Martin, we place an emphasis on empowering our employees by fostering innovation, integrity, and exemplifying the epitome of corporate responsibility. Your Mission is Ours. When it comes to using cutting-edge to tackle complex problems, Lockheed Martin is driven by a singular mission focus and desire to continuously innovate. Today’s challenges to global security aren’t just changing – they’re accelerating faster than ever before. Through our dedication to our mission, our AI-enabled systems are changing the way militaries operate and protect their forces, the way first responders fight fires, and how researchers explore the far reaches of space and the ocean’s depths. The Lockheed Martin Artificial Intelligence Center (LAIC) team is seeking a motivated intern with hands-on experience in software engineering and to execute on and grow the LAIC's portfolio. The LAIC's mission includes serving as a center of excellence for AI at Lockheed Martin both internally and externally facing. The candidate will work to disseminate best practices of the LAIC, build prototypes of new capabilities, and be a part of larger product teams solving larger-scale integrations. The candidate will be required to create knowledge materials whenever applicable and publish these best practices, in addition to meeting with other Lockheed Martin community members to discuss. The candidate will need a combination of business focus, creative thinking, strong analytical and problem solving skills, and programming knowledge to be able to quickly cycle hypotheses through the discovery phase of the project. Successful candidates demonstrate excitement to learn new technologies and methodologies, while also being a collaborative teammate. Excellent communications skills are required to understand the needs of the business and to communicate the value of the models built back to the business. Domestic travel may be required for customer interactions and team meetings (<10%).

Requirements

Pursuing a Bachelor's degree in Computer Science, Engineering, Electrical Engineering, Computer Engineering, Mathematics, Information Sciences and Technology, or STEM related degree from an accredited college
Prior coursework or project experience in computer science, data science, or machine learning
Experience with Python programming language
US Citizenship required due to system access

Nice To Haves

Experience with DevSecOps software development practices
Experience with Go, and/or Bash
Experience in applying any of the following techniques to business problems: supervised regression/classification, natural language processing, deep learning, anomaly detection, and reinforcement learning
Experience evaluating model performance, including appropriate performance metric selection
Familiarity with Kubernetes (ex: Container Storage Interfaces (CSI), Persistent Volumes, Helm/Kustomize)
Familiarity with Pipeline and GitOps Automation, such as ArgoCD, Tekton, Gitlab CI/CD

Responsibilities

Collaborating with business stakeholders to identify business opportunities and requirements
Developing associated statistical models that deliver useful, actionable recommendations, or which augment human capabilities in various work settings
Modeling complex business and engineering problems
Discovering insights and deploying AI capabilities using and associated feature engineering techniques
Tracking projects and driving work on them through to completion
Implementing best technical practices from the fields of and software engineering