Senior Machine Learning Engineer - Secure AI Lab

Carnegie Mellon University•Pittsburgh, PA

1d•Onsite

About The Position

The Senior Machine Learning Engineer will work in the Secure AI Lab within Carnegie Mellon University’s Software Engineering Institute (SEI) AI Division, developing engineering solutions that support research into vulnerabilities in AI and machine-learning algorithms and help secure systems against those vulnerabilities. The lab brings together machine-learning research scientists, engineers, and software developers to improve the security and robustness of AI systems against adversaries. The role involves solving problems for government sponsors by analyzing, designing, and building responsible AI systems. Work includes investigating emerging AI technologies; developing processes, practices, and tools for working with AI; building prototypes; and transitioning AI capabilities to government sponsors. The engineer will work across research areas including counter-AI research, defenses for AI and machine-learning algorithms, and applied adversarial machine learning. The position includes developing machine-learning models and systems, data pipelines, ETL processes, and backend systems; experimenting with frameworks, methods, and algorithms; and evaluating system performance and security through prototyping, testing, and analysis. The engineer will collaborate with developers, researchers, designers, technical leads, and government customers, and will mentor and teach colleagues. The position is based onsite at the SEI’s Pittsburgh, Pennsylvania, or Arlington, Virginia, offices five days per week, with travel of up to 25% to locations outside the employee’s home location.

Requirements

Bachelor’s degree in computer science, statistics, machine learning, electrical engineering, or a related discipline, plus 10 years of experience; or a master’s degree in one of these fields plus 8 years of experience; or a Ph.D. in a relevant field plus 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 prior work to build upon.
Strong written and verbal communication skills, including the ability to explain complex technical ideas in layperson’s terms.
Experience publishing written or technical artifacts showcasing one’s work.
Strong collaboration skills for working with colleagues and sponsors.
Willingness to guide and mentor junior team members.
Must be able to obtain and maintain an active Department of War security clearance and undergo a background investigation.
Must be willing 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 is desirable but not required.

Responsibilities

Build machine-learning models and systems using frameworks such as TensorFlow, PyTorch, Torch, and Caffe, and languages such as 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.
Identify and investigate emerging AI and AI-adjacent technologies, and define and refine processes, practices, and tools for working with AI.
Collaborate with developers, researchers, designers, technical leads, and government customers to understand challenges, needs, and possible 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.