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Adversarial Machine Learning Jobs in Washington (NOW HIRING)

C-Serv

AI Offensive Security Engineer

Washington, DC · On-site

This role sits at the intersection of adversarial machine learning, enterprise security architecture, and governance. You will lead the design and execution of structured red team engagements across ...

Cmu

Senior AI Security Software Engineer

Arlington, VA · 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

Arlington, VA · 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 ...

10a Labs

Machine Learning Engineer

Washington, DC · On-site

$130K - $200K/yr

Our adversarial red teaming, model evaluations, and intelligence collection enable engineering ... We are seeking a Machine Learning Engineer (3-5+ years of experience) to help design, build ...

10a Labs

Machine Learning Engineer

Washington, DC · On-site +1

$130K - $200K/yr

Our adversarial red teaming, model evaluations, and intelligence collection enable engineering ... We are seeking a Machine Learning Engineer (3-5+ years of experience) to help design, build ...

TheIncLab

Senior Machine Learning Engineer

Mclean, VA · On-site

$105K - $145K/yr

Transformer-based models, adversarial networks, genetic algorithms * Retrieval-Augmented Generation (RAG) where appropriate * Design and implement machine learning models using frameworks such as ...

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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.
What are popular job titles related to Adversarial Machine Learning jobs in Washington? For Adversarial Machine Learning jobs in Washington, the most frequently searched job titles are:
What job categories do people searching Adversarial Machine Learning jobs in Washington look for? The top searched job categories for Adversarial Machine Learning jobs in Washington are:
What cities in Washington are hiring for Adversarial Machine Learning jobs? Cities in Washington with the most Adversarial Machine Learning job openings:
Adversarial Machine Learning jobs near you

Associate Machine Learning Engineer - Secure AI Lab

Cmu

Arlington, VA

Full-time

Posted 18 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 toprovideleap-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, andmaintainingAI capabilities

  • identifyand 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 supportresearchinto the vulnerabilities of AIandML algorithms and securing against those vulnerabilities.

TheSecure AILab 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, theSecure AILabconducts and appliescutting-edgeresearch toprotectAI systems fromadversaries who aim to manipulatethe systemto learn, do, or revealsomething itisn'tsupposed to.

TheSecure AILab 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 modelstargeting AIandML algorithms,understand the behaviors of AI algorithms,identifyweak points, and design novel ways to subvert AIandMLsystems.

  • AIandMLAlgorithm DefenseResearch:Createpractical mitigations and defenses forobservedattacksaffecting AIandML algorithmsand evaluate the effectiveness ofdefensivetechniques.

  • 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:

  • Identifyingandinvestigatingemerging AI and AI-adjacent technologies.

  • Defining andrefiningprocesses, practices, and tools for working with AI.

  • Designing andbuildingwell-engineered prototypes of AI systems.

  • Transitioning andprovidingguidance onAI 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 datapipelines, ETL processes, and backend systems. You will work with, extend, and implementstate-of-the-artmachine 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,planningand scheduling, robot control, and engineering safe, trusted, and reliable machine learning systems.

  • Testingand evaluation.You'llconduct rapid prototyping todemonstrateand evaluate technologies in relevant environments.You'llevaluate systems for performance and security.You'lltest capabilities using novel testing and analysis techniques.

  • Collaboration.You'llactivelyparticipateon teams of developers, researchers, designers, and technical leads.You'llcollaborate with researchers and our government customers to understand challenges, needs, andpossible solutions.

  • Mentoring.You'llcontribute to improving the overall technical capabilities of the Division by mentoring and teaching others,participatingin design (software and otherwise) sessions, and sharing insights and wisdom across the SEI.

Knowledge andExperience

  • Comprehensiveknowledge ofmachine learning;previousexperiencein adversarial machine learningdesirablebut notrequired

  • A track recordofusingwell-establishedengineering practices to solvedifficult problems

  • An understanding ofhow toconvertresearch resultsintofunctioning prototypesor capabilities

  • Experienceleadingtechnicalprojectsinnovelareaswith limitedpreviouswork to build upon

  • Strong written and verbal communication skills;able to convey complex technical ideasinalayperson's terms

  • Ampleexperience with publishingwritten or technicalartifactsshowcasingyour work

  • Strong collaboration skills for working with colleagues and sponsors

  • Willingnesstoguide andmentorjunior 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 andmaintainan 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

About CMU

Sourced by ZipRecruiter

Industry

Offices of mental health practitioners

Company size

201 - 500 Employees

Headquarters location

Harrisburg, PA, US

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