Machine Learning Engineer - Secure AI Lab

Carnegie Mellon University•Pittsburgh, PA

14h•Onsite

About The Position

The Machine Learning Engineer will work in the Secure AI Lab within the SEI AI Division, engineering solutions that support research into vulnerabilities in AI and machine learning algorithms and help secure systems against those vulnerabilities. The lab conducts research and applies advanced techniques to protect AI systems from adversaries, with work spanning counter-AI research, algorithm defenses, and applied adversarial machine learning. The engineer will solve problems for government sponsors by analyzing, designing, and building responsible AI systems. Work includes investigating emerging AI technologies; defining processes, practices, and tools for AI; building prototypes; and transitioning capabilities to government sponsors. The role involves developing machine learning models and systems, working with data pipelines and backend systems, experimenting with methods across application domains, and testing systems for performance and security. The engineer will collaborate with researchers, developers, designers, technical leads, and government customers, and will contribute to the division through mentoring and sharing technical knowledge. The position is based onsite at SEI offices in Pittsburgh, Pennsylvania, or Arlington, Virginia, five days per week, and involves travel up to 25% of the time. Candidates must be able to obtain and maintain an active Department of War security clearance and will be subject to a background investigation.

Requirements

A bachelor's degree in computer science, statistics, machine learning, electrical engineering, or a related discipline, with eight years of experience; or a master's degree in one of those fields with five years of experience; or a Ph.D. with two years of experience.
Comprehensive knowledge of machine learning.
A 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 prior work to build upon.
Strong written and verbal communication skills, including the ability to explain complex technical ideas in layperson's terms.
Substantial 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 and pass a background investigation.
Willingness to work onsite at the SEI's Pittsburgh, PA, or Arlington, VA, offices 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.

Responsibilities

Build machine learning models and systems using frameworks such as TensorFlow, PyTorch, Torch, and Caffe, and programming languages including Python, C/C++, and Java.
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 in areas 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 teams of developers, researchers, designers, and technical leads.
Work with researchers and government customers to understand challenges, needs, and potential solutions.
Transition AI capabilities to government sponsors and provide guidance on those capabilities.
Mentor and teach others, participate in design sessions, and share technical insights across the SEI.
Identify and investigate emerging AI and AI-adjacent technologies, and help define processes, practices, and tools for working with AI.