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Ai For Science Jobs in Virginia (NOW HIRING)

MORSE Corp

Data Scientist- TS/SCI

Arlington, VA · On-site

They are searching for a Data Scientist with expertise in data analysis and algorithm development to provide insights into applying AI for national security problems. Responsibilities : • Perform ...

Cmu

Autonomy Engineer

Arlington, VA · Hybrid

The AI for Autonomy Lab researches and demonstrates the application of AI-related technologies for ... PhD in Computer Science with two (2) years of experience. * This position is based onsite 5 days ...

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Ai For Science information

How does collaboration typically work between AI for Science professionals and domain experts in research teams?

AI for Science professionals frequently work closely with experts in fields such as biology, chemistry, or physics to identify scientific problems that can benefit from machine learning techniques. Collaboration usually involves regular meetings to translate complex scientific challenges into data-driven models, sharing domain knowledge, and iteratively refining solutions. Effective communication and a willingness to bridge gaps between computational and scientific perspectives are essential. This interdisciplinary teamwork not only enhances the impact of AI solutions but also fosters ongoing learning and innovation.

What is AI for Science?

AI for Science refers to the application of artificial intelligence and machine learning techniques to accelerate scientific discovery and research. By leveraging large datasets, complex models, and advanced computational methods, AI helps scientists analyze data, identify patterns, simulate experiments, and make predictions across various scientific fields such as biology, chemistry, physics, and climate science. This approach can significantly speed up research, uncover new insights, and solve problems that were previously too complex or time-consuming for traditional methods.

Which AI for science is best?

The best AI tools for science depend on the specific application, such as data analysis, modeling, or simulation. Popular options include TensorFlow, PyTorch, and specialized platforms like DeepMind or IBM Watson, which are used by researchers to develop and deploy AI models in scientific research. Proficiency in programming languages like Python and understanding of machine learning concepts are essential for roles in AI for science.

What are the key skills and qualifications needed to thrive as an AI for Science specialist, and why are they important?

To thrive as an AI for Science Specialist, you need a strong background in computer science, mathematics, and scientific domains, often supported by advanced degrees (e.g., PhD or MSc) in relevant fields. Proficiency with machine learning frameworks (such as TensorFlow or PyTorch), scientific computing tools, and familiarity with high-performance computing environments are typically required. Critical thinking, interdisciplinary collaboration, and effective communication are crucial soft skills for translating scientific problems into AI solutions. These skills are vital for developing innovative models, ensuring research rigor, and enabling impactful scientific discoveries.

What is the difference between Ai For Science vs Data Scientist?

AspectAi For ScienceData Scientist
Required CredentialsDegree in Science, Computer Science, or related fields; knowledge of AI and machine learningDegree in Statistics, Computer Science, or related fields; strong programming skills
Work EnvironmentResearch labs, scientific institutions, tech companies focused on scientific applicationsCorporate, tech firms, finance, healthcare, and other industries analyzing data
Industry UsageApplied to scientific research, simulations, and experimental data analysisUsed for data analysis, predictive modeling, and business insights

Ai For Science focuses on applying AI techniques to scientific research and experiments, often requiring a background in science and specialized knowledge of AI. Data Scientists analyze large datasets across various industries to extract insights and build models. While both roles involve AI and data analysis, Ai For Science is more research-oriented within scientific contexts, whereas Data Scientists work across diverse sectors on data-driven decision making.

What are popular job titles related to Ai For Science jobs in Virginia? For Ai For Science jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Ai For Science jobs in Virginia look for? The top searched job categories for Ai For Science jobs in Virginia are:
Ai For Science jobs near you
Infographic showing various Ai For Science job openings in Virginia as of August 2026, with employment types broken down into 1% Internship, 1% As Needed, 72% Full Time, 23% Part Time, and 3% Contract. Highlights an 76% Physical, 3% Hybrid, and 21% Remote job distribution.

Senior Director - AI & Proactive Security

S&P Global

Centreville, VA • On-site

Full-time

Posted 21 days ago

S&P Global rating

7.3

Company rating: 7.3 out of 10

Based on 10 frontline employees who took The Breakroom Quiz

Job description

Senior Director - AI & Proactive Security
We are seeking a highly innovative and forward-leaning leader to drive the execution of the organization's AI-focused cyber resilience capabilities. This role sits at the center of securing AI systems and leveraging AI to transform cybersecurity operations. The position is responsible for operationalizing AI security (security for AI systems) and AI-driven security innovation (AI for security), translating enterprise strategy into tangible capabilities across red teaming, continuous validation, and AI-enabled defense. This role requires a disruptive mindset, strong technical depth, and the ability to challenge traditional security approaches in favor of modern, AI-driven models.
Operational Leadership
  • Own execution of cyber resilience initiatives aligned to defined strategy
  • Design and champion a Continuous Threat Exposure Management strategy that integrates offensive security insights into enterprise risk management.
  • Define and own the enterprise AI security strategy, aligning with organizational security philosophy, risk appetite, and business objectives.
  • Partner with leaders across Cyber Defense, Architecture and Engineering, Identity and Access Management, and Governance, Risk and Compliance to transform operations through AI and offensive security insights.
  • Track performance metrics and effectiveness of AI security and testing programs
  • Coordinate internal teams and external vendors to deliver outcomes
  • Provide actionable insights and reporting to senior leadership

AI Security - Security for AI Systems
  • Lead execution of AI security controls across model lifecycle (development, deployment, monitoring)
  • Perform AI red teaming to identify vulnerabilities such as prompt injection, model inversion, and adversarial inputs
  • Implement and operate AI Security Posture Management (AISPM) capabilities
  • Support governance of AI models, including model inventory, access controls, and usage monitoring
  • Partner with engineering and data science teams to embed security controls into MLOps pipelines
  • Validate security of cloud AI platforms (e.g., AWS Bedrock, GCP Vertex AI) and internal AI systems

AI for Security - Transformation & Innovation
  • Drive adoption of AI capabilities to enhance threat detection, response, and automation
  • Prototype and deploy AI/ML use cases across cyber defense, GRC, and offensive security
  • Partner to drive adoption of AI and machine learning capabilities across GRC, Cyber Defense, IAM, Architecture and Engineering, and Offensive Security.
  • Evaluate and operationalize AI-powered security tools and platforms
  • Develop internal AI literacy and capability uplift programs for security teams to responsibly and effectively leverage AI technologies
  • Collaborate with internal teams to embed AI into security workflows and decision-making processes
  • Challenge conventional security models and introduce innovative, automation-driven approaches

Offensive Security & Continuous Validation
  • Execute red and purple team exercises simulating advanced adversary behaviors
  • Conduct adversary emulation campaigns aligned to business-critical assets
  • Integrate offensive testing results into control improvements and detection engineering
  • Operate breach and attack simulation tools to continuously validate controls
  • Support risk-based vulnerability management with a focus on exploitability and business impact
  • Continuously assess attack surface, including AI systems and shadow AI usage
  • Translate testing results into measurable risk reduction actions

Qualifications
  • 15+ years in cybersecurity with some experience or exposure to AI/ML or emerging technologies
  • Hands-on experience with offensive security, red teaming, or advanced security testing
  • Strong understanding of AI/ML concepts and associated security risks
  • Experience working across security engineering, defense, and risk functions
  • Demonstrated ability to innovate and challenge traditional security models

Preferred Qualifications
  • Experience with AI red teaming or AI model security
  • Familiarity with MLOps pipelines and AI system architecture
  • Experience with cloud AI platforms and modern architectures
  • Background in highly regulated industries or complex environments

Success Measures: First 12-18 Months
  • Stand up core AI Security capabilities, including AI red teaming, AI Security Posture Management (AISPM), and baseline controls across AI/ML environments.
  • Establish an operational model for securing AI systems, including repeatable testing methodologies for model risk (e.g., prompt injection, adversarial inputs, data leakage scenarios).
  • Deliver initial AI red teaming campaigns against priority AI use cases, models, and platforms, with findings integrated into remediation workflows.
  • Embed security into AI/ML pipelines by partnering with engineering and data science teams.
  • Drive adoption of AI-enabled security capabilities, delivering 2-3 production use cases improving detection, response, or automation.
  • Introduce continuous validation for AI and traditional environments using BAS and offensive techniques.
  • Reduce measurable risk exposure by integrating findings into vulnerability management and remediation processes.
  • Identify and assess shadow AI usage and bring it under governance and security controls.
  • Evaluate and operationalize key AI security tools delivering measurable improvement in outcomes.
  • Influence a shift toward AI-driven security models and innovative approaches.
  • Establish KPIs demonstrating improvements in AI security maturity and cyber resilience.

Compensation/Benefits Information (US Applicants Only): Final base salary for this role will be based on the individual's geographic location, as well as experience level, skill set, training, licenses, and certifications. In addition to base compensation, this role is eligible for an annual incentive plan. This role is eligible to receive additional S&P Global benefits. For more information on the benefits that we provide to our employees, please click here.
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!
It is the policy of Mobility to provide equal employment opportunity (EEO) to all persons regardless of age, color, national origin, citizenship status, physical or mental disability, race, religion, creed, gender, sex, sexual orientation, gender identity and/or expression, genetic information, marital status, status with regard to public assistance, veteran status, or any other characteristic protected by federal, state or local law. In addition, Mobility will provide reasonable accommodations for qualified individuals with disabilities.

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