At the SEI AI Division, we conduct research in applied artificial intelligence and the engineering questions related to the practical design and implementation of AI technologies and systems. We currently lead a community-wide movement to mature the discipline of AI Engineering for Defense and National Security.
As our government customers adopt AI and machine learning to provide leap-ahead mission capabilities, we
build real-world, mission-scale AI capabilities through solving practical engineering problems
discover and define the processes, practices, and tools to support operationalizing AI for robust, secure, scalable, and human-centered mission capabilities
prepare our customers to be ready for the unique challenges of adopting, deploying, using, and maintaining AI capabilities
identify and investigate emerging AI and AI-adjacent technologies that are rapidly transforming the technology landscape
Are you creative, curious, energetic, collaborative, technology-focused, and hard-working? Are you interested in making a difference by bringing innovation to government organizations and beyond? Apply to join our team.
Overview: As an Associate Machine Learning Engineer, you will specialize in engineering solutions that support research into the vulnerabilities of AI and ML algorithms and securing against those vulnerabilities.
The Secure AI Lab within the SEI’s AI Division focuses on improving the security and robustness of AI systems. As part of the world-class research community at Carnegie Mellon University, the Secure AI Lab conducts and applies cutting-edge research to protect AI systems from adversaries who aim to manipulate the system to learn, do, or reveal something it isn’t supposed to.
The Secure AI Lab consists of machine learning research scientists, machine learning engineers, and software developers who work together to solve problems in the following areas:
Counter AI Research: Study threat models targeting AI and ML algorithms, understand the behaviors of AI algorithms, identify weak points, and design novel ways to subvert AI and ML systems.
AI and ML Algorithm Defense Research: Create practical mitigations and defenses for observed attacks affecting AI and ML algorithms and evaluate the effectiveness of defensive techniques.
Applied Adversarial Machine Learning: Advance the state of the art in adversarial machine learning by developing and transitioning capabilities to government sponsors.
As an engineer, you will solve problems for government sponsors by analyzing, designing, and building responsible AI systems.
Your day-to-day engineering tasks will include:
Identifying and investigating emerging AI and AI-adjacent technologies.
Defining and refining processes, practices, and tools for working with AI.
Designing and building well-engineered prototypes of AI systems.
Transitioning and providing guidance on AI capabilities to government sponsors.
Duties
Building Machine Learning Models and Systems: You will work with machine learning frameworks such as TensorFlow, PyTorch, Torch, and Caffe and modern programming languages including Python, C/C++, and Java. You will build and work with data pipelines, ETL processes, and backend systems. You will work with, extend, and implement state-of-the-art machine learning methods.
Technical Experimentation: You will experiment with modern and emerging machine learning frameworks, methods, and algorithms in application domains that include computer vision, natural language processing, planning and scheduling, robot control, and engineering safe, trusted, and reliable machine learning systems.
Testing and evaluation. You'll conduct rapid prototyping to demonstrate and evaluate technologies in relevant environments. You'll evaluate systems for performance and security. You'll test capabilities using novel testing and analysis techniques.
Collaboration. You'll actively participate on teams of developers, researchers, designers, and technical leads. You'll collaborate with researchers and our government customers to understand challenges, needs, and possible solutions.
Mentoring. You'll contribute to improving the overall technical capabilities of the Division by mentoring and teaching others, participating in design (software and otherwise) sessions, and sharing insights and wisdom across the SEI.
Knowledge and Experience
Comprehensive knowledge of machine learning; previous experience in adversarial machine learning desirable but not required
A track record of using well-established engineering practices to solve difficult problems
An 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; able to convey complex technical ideas in a layperson’s terms
Ample experience with publishing written or technical artifacts showcasing your work
Strong collaboration skills for working with colleagues and sponsors
Willingness to guide and mentor junior team members
Requirements
A bachelor’s degree in computer science, statistics, machine learning, electrical engineering, or related discipline with three (3) years of experience; OR MS in the same fields with one (1) year of experience; OR PhD in a relevant discipline.
Willingness to work onsite 5 days per week at SEI offices in Pittsburgh, PA or Arlington, VA.
You will be subject to a background investigation and must be able to obtain and maintain an active Department of War security clearance.
Willing to travel up to 25% of the time to locations outside of your home location. Travel sites include SEI offices in Pittsburgh and Washington, D.C., sponsor sites, and conferences.
Location
Arlington, VA, Pittsburgh, PAJob Function
Software/Applications Development/EngineeringPosition Type
Staff – RegularFull time/Part time
Full timePay Basis
SalaryPlease visit “Why Carnegie Mellon” to learn more about becoming part of an institution inspiring innovations that change the world.
Click here to view a listing of employee benefits
Carnegie Mellon University is an Equal Opportunity Employer/Disability/Veteran.