Machine Learning Engineer

Build responsible, secure, and robust AI while supporting government-focused research at Carnegie Mellon University’s SEI AI Division. This onsite role in Pittsburgh is centered on engineering solutions that help investigate vulnerabilities in AI/ML algorithms and strengthen defenses against them, with a strong emphasis on turning research into practical prototypes and capabilities.

What you’ll work on

• Study threat models targeting AI and ML algorithms, understand algorithm behaviors, identify weak points, and design novel ways to subvert AI and ML systems.
• Create practical mitigations and defenses for observed attacks affecting AI/ML algorithms and evaluate defensive effectiveness.
• Advance applied adversarial machine learning by developing and transitioning capabilities to government sponsors.

Responsibilities

• Identify and investigate emerging AI and AI-adjacent technologies.
• Define and refine processes, practices, and tools for working with AI.
• Design and build well-engineered prototypes of AI systems.
• Transition and provide guidance on AI capabilities to government sponsors.
• Build machine learning models and systems using frameworks and languages including TensorFlow, PyTorch, Torch, Caffe and Python, C/C++, and Java; 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 frameworks, methods, and algorithms across domains including computer vision, natural language processing, planning and scheduling, and robot control, with a focus on safe, trusted, and reliable machine learning systems.
• Conduct rapid prototyping to demonstrate and evaluate technologies in relevant environments; evaluate systems for performance and security; test capabilities using novel testing and analysis techniques.
• Collaborate with developers, researchers, designers, and technical leads; partner with researchers and government customers to understand challenges, needs, and possible solutions.
• Strengthen the Division’s technical capabilities through mentoring and teaching, participation in design sessions (software and otherwise), and sharing technical insights.

Required qualifications

• Comprehensive knowledge of machine learning; prior experience with adversarial machine learning is desirable (not required).
• Demonstrated use of well-established engineering practices to solve difficult problems.
• Ability 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 explaining complex technical ideas in a layperson’s terms.
• Experience publishing written or technical artifacts showcasing your work.
• Strong collaboration skills with colleagues and sponsors.
• Willingness to guide and mentor junior team members.
• Education and experience: bachelor’s degree with 10 years of experience, or MS with 8 years, or PhD with 5 years (in computer science, statistics, machine learning, electrical engineering, or a related discipline).
• Subject to a background investigation and able to obtain and maintain an active Department of War security clearance.
• Willingness to work onsite at SEI offices in Pittsburgh, PA or Arlington, VA, 5 days per week.
• Willingness to travel up to 25% of the time to locations outside of your home location.

Technologies you’ll use

• TensorFlow, PyTorch, Torch, Caffe
• Python, C/C++, Java
• Computer vision, natural language processing, planning and scheduling, robot control

Benefits

• Comprehensive medical, prescription, dental, and vision insurance
• Generous retirement savings program with employer contributions
• Tuition benefits
• Paid time off and observed holidays
• Life and accidental death and disability insurance
• Free Pittsburgh Regional Transit bus pass
• Access to a Family Concierge Team to help navigate childcare needs
• Fitness center access

Location: Pittsburgh, PA (onsite). Position type: Staff – Regular. Work arrangement: Full-time. Pay basis: Salary.