Machine Learning Engineer - Secure AI Lab

Software Engineering Institute | Carnegie Mellon University•Pittsburgh, PA

9d•Onsite

About The Position

The Machine Learning Engineer will engineer solutions supporting research into vulnerabilities in AI and machine learning algorithms and help secure systems against those vulnerabilities. The role is part of the Secure AI Lab in the SEI AI Division, which conducts research and develops practical methods to improve the security and robustness of AI systems. The lab brings together machine learning research scientists, engineers, and software developers to study threats, develop defenses, and advance adversarial machine learning capabilities for government sponsors. The engineer will analyze, design, and build responsible AI systems for government sponsors. Work includes investigating emerging technologies; developing processes, practices, and tools for AI engineering; creating prototypes; transitioning capabilities and providing guidance to sponsors; and evaluating systems for performance and security. The work spans domains such as computer vision, natural language processing, planning and scheduling, robot control, and safe, trusted, and reliable machine learning. The engineer collaborates with developers, researchers, designers, technical leads, and government customers, and contributes through mentoring and sharing technical knowledge. The position is a regular, full-time staff role. It requires working onsite five days per week at the SEI offices in Pittsburgh, Pennsylvania, or Arlington, Virginia, and travel of up to 25% to locations outside the employee’s home location. The employee must undergo a background investigation and be able to obtain and maintain an active Department of War security clearance.

Requirements

Bachelor’s degree in computer science, statistics, machine learning, electrical engineering, or a related discipline and 10 years of experience; or a master’s degree in the same fields and 8 years of experience; or a PhD and 5 years of experience.
Comprehensive knowledge of machine learning.
Track record of using well-established engineering practices to solve difficult problems.
Understanding of how to convert research results into functioning prototypes or capabilities.
Experience leading technical projects in novel areas with limited previous work to build upon.
Strong written and verbal communication skills, including the ability to explain complex technical ideas in layperson’s terms.
Ample experience publishing written or technical artifacts showcasing work.
Strong collaboration skills for working with colleagues and sponsors.
Willingness to guide and mentor junior team members.
Ability to obtain and maintain an active Department of War security clearance, subject to a background investigation.
Willingness to work onsite at the SEI offices in Pittsburgh, PA, or Arlington, VA, five days per week.
Willingness to travel up to 25% of the time to locations outside the employee’s home location.

Nice To Haves

Previous experience in adversarial machine learning is desirable but not required.

Responsibilities

Build machine learning models and systems using machine learning frameworks and modern programming languages.
Build and work with data pipelines, ETL processes, and backend systems.
Work with, extend, and implement state-of-the-art machine learning methods.
Experiment with modern and emerging machine learning frameworks, methods, and algorithms across application domains including computer vision, natural language processing, planning and scheduling, robot control, and safe, trusted, and reliable machine learning systems.
Rapidly prototype technologies and demonstrate and evaluate them in relevant environments.
Evaluate systems for performance and security, and test capabilities using novel testing and analysis techniques.
Collaborate with developers, researchers, designers, technical leads, and government customers to understand challenges, needs, and potential solutions.
Identify and investigate emerging AI and AI-adjacent technologies.
Define and refine processes, practices, and tools for working with AI.
Transition AI capabilities to government sponsors and provide guidance.
Mentor and teach others, participate in design sessions, and share technical insights across the SEI.