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Adversarial Machine Learning Jobs in Pennsylvania

Cmu

Senior AI Security Software Engineer

Pittsburgh, PA · On-site +1

Additionally, we work in generative AI and large language models, data visualization, security analysis of AI systems, and adversarial machine learning. We have access to a wide variety of cyber ...

Cmu

AI Security Software Engineer

Pittsburgh, PA · On-site +1

Additionally, we work in generative AI and large language models, data visualization, security analysis of AI systems, and adversarial machine learning. We have access to a wide variety of cyber ...

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Adversarial Machine Learning information

What are some common challenges faced by professionals working in adversarial machine learning roles?

Adversarial Machine Learning professionals often face the challenge of staying ahead of rapidly evolving attack techniques that can compromise model integrity and security. Managing the balance between model performance and robustness is another key difficulty, as defenses against adversarial attacks can sometimes reduce accuracy or increase computational costs. Collaboration with data scientists, security teams, and software engineers is vital for developing resilient models and implementing effective defenses. Staying current with the latest research and tools is essential for success in this dynamic field.

What are the key skills and qualifications needed to thrive as an adversarial machine learning specialist, and why are they important?

To excel in Adversarial Machine Learning, you need a strong background in machine learning, deep learning, statistics, and computer science, typically supported by an advanced degree in a related field. Familiarity with frameworks like TensorFlow or PyTorch, experience with adversarial attack and defense libraries, and knowledge of security protocols are crucial. Creative problem-solving, critical thinking, and strong communication skills help in designing robust models and explaining complex threats to stakeholders. These competencies are vital to anticipate vulnerabilities, safeguard AI systems, and ensure the reliability of machine learning models in real-world applications.

What is the difference between Adversarial Machine Learning vs Data Scientist?

AspectAdversarial Machine LearningData Scientist
CredentialsKnowledge of machine learning, cybersecurity, and threat detectionDegree in data science, statistics, or related fields
Work EnvironmentResearch labs, cybersecurity teams, AI developmentBusiness analytics, data analysis, model development
Industry UsageAI security, cybersecurity, machine learning researchBusiness, finance, healthcare, tech companies

Adversarial Machine Learning focuses on understanding and defending AI models against malicious inputs, often within cybersecurity contexts. Data Scientists analyze data to extract insights, build models, and support decision-making across various industries. While both roles require machine learning knowledge, Adversarial Machine Learning emphasizes security and robustness, whereas Data Scientists focus on data analysis and predictive modeling.

What is adversarial machine learning?

Adversarial machine learning is a field of study focused on understanding and defending against attacks that manipulate machine learning models by feeding them deceptive input, known as adversarial examples. These attacks can cause models to make incorrect predictions, raising concerns about the security and reliability of AI systems, especially in critical applications like image recognition and autonomous vehicles. Researchers in this area develop techniques to detect, prevent, and mitigate these vulnerabilities to make machine learning systems more robust.
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Adversarial Machine Learning jobs near you

Senior Machine Learning Research Scientist - Secure AI Lab

Software Engineering Institute

Pittsburgh, PA • On-site

$95K - $121K/yr

Full-time

Re-posted 11 days ago

Job description

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 a Senior Machine Learning Research Scientist, you will specialize in conducting 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.

Your day-to-day research tasks will include:
  • Identifying and investigating emerging AI and AI-adjacent technologies.

  • Performing and publishing impactful original research in the field of AI and ML algorithm vulnerabilities and securing against those vulnerabilities.

  • Adapting and applying existing research in the field to solve real-world problems.

  • Transitioning and providing guidance onAI capabilities to government sponsors.

Duties:
  • Hands-on research: You'll conduct and lead novel research in applied machine learning and artificial intelligence.

  • Solution development: You'll work with and lead interdisciplinary teams to turn research results into prototype operational capabilities for government customers and stakeholders.

  • Strategy: You'll work with the leadership team and colleagues to plan, develop, and carry out an overall research strategy, and to influence the national research agenda regarding future technology.

  • Collaboration: You'll actively participate on teams of software developers, researchers, designers, and technical leads. You'll build relationships and collaborate with researchers, government customers, and other stakeholders to understand challenges, needs, possible solutions, and research directions.

  • 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 AI Division.

Knowledge and Experience
  • Comprehensive knowledge of machine learning; previous experience in adversarial machine learning preferred but not required

  • A track record of conducting research and applying scientific methods to solve difficult problems

  • Experience leading research projects in novel areas with limited previous work to build upon

  • Ability to work with leadership to plan, develop, and deliver an overall research strategy

  • Strong written and verbal communication skills; ability to convey complex technical ideas in a layperson's terms

  • Proficiency in writing funding proposals or pitching ideas for new research projects

  • 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 ten (10) years of experience; OR MS in the same fields with eight (8) years of experience; OR PhD with five (5) years of experience
  • Willingness to work onsite at an SEI facility 5 days per week.

  • 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, PA
Job Function
Software/Applications Development/Engineering
Position Type
Staff - Regular
Full time/Part time
Full time
Pay Basis
SalaryMore Information:
  • Please 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.
  • Statement of Assurance

Apply