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Applied Machine Learning Jobs in Virginia (NOW HIRING)

Capital One

Lead Machine Learning Engineer

Mclean, VA · On-site

$103K - $136K/yr

Lead Machine Learning Engineer At Capital One, we are changing banking for good by creating ... We are committed to continuing to build world-class applied science and engineering teams to ...

Capital One

Lead Machine Learning Engineer

Richmond, VA

$101K - $133K/yr

Lead Machine Learning Engineer At Capital One, we are changing banking for good by creating ... We are committed to continuing to build world-class applied science and engineering teams to ...

Socket.dev

Senior Data Scientist

Triangle, VA · On-site

$140 - $190/hr

Operating at the intersection of applied machine learning, business strategy, and the modern data platform (Microsoft Fabric, OneLake, and Azure), this role designs, builds, and deploys predictive ...

Radiance Technologies

Data Scientist

Dahlgren, VA · On-site

Strong knowledge of data imputation techniques and applied machine learning * Familiarity with defense-related analytics or operational modeling preferred At Radiance Technologies , your work has ...

Radiance Technologies

Data Scientist

Dahlgren, VA · On-site

Strong knowledge of data imputation techniques and applied machine learning * Familiarity with defense-related analytics or operational modeling preferred At Radiance Technologies , your work has ...

Cmu

AI Security Software Engineer

Arlington, VA · On-site +1

Familiarity with applied machine learning domains (e.g., natural language processing, computer vision, autonomy, audio analysis) * Experience and knowledge in cybersecurity best practices

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Applied Machine Learning information

See Virginia salary details

$25.3K

$42.2K

$87.2K

How much do applied machine learning jobs pay per year?

As of Aug 10, 2026, the average yearly pay for applied machine learning in Virginia is $42,218.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,200.00 and $45,600.00 per year, depending on experience, location, and employer.

What are the typical collaboration dynamics between applied machine learning engineers and other teams within a company?

Applied Machine Learning engineers often work closely with cross-functional teams including data scientists, software engineers, product managers, and business analysts. They are typically responsible for translating business problems into machine learning solutions and ensuring models are effectively integrated into production systems. This role requires frequent communication to align on project goals, share progress, and address technical challenges, making teamwork and stakeholder management crucial for successful deployments and continuous improvement.

What is applied machine learning?

Applied machine learning involves using machine learning techniques and algorithms to solve real-world problems in various industries, such as healthcare, finance, and technology. Practitioners focus on selecting appropriate models, preparing data, training algorithms, and deploying solutions that deliver tangible value. Unlike theoretical machine learning, applied machine learning emphasizes practical implementation, evaluation, and optimization to meet business or research objectives.

What are the key skills and qualifications needed to thrive as an applied machine learning professional?

To excel in Applied Machine Learning, you need a solid background in mathematics, statistics, computer science, and experience with machine learning algorithms, often supported by a relevant degree or certification. Familiarity with programming languages like Python or R, frameworks such as TensorFlow or PyTorch, and version control systems is typically required. Strong problem-solving abilities, communication skills, and a collaborative mindset help you interpret results and convey insights to diverse stakeholders. These competencies are crucial for building effective models, driving data-driven decisions, and ensuring the successful integration of machine learning solutions into real-world applications.
What are popular job titles related to Applied Machine Learning jobs in Virginia? For Applied Machine Learning jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Applied Machine Learning jobs in Virginia look for? The top searched job categories for Applied Machine Learning jobs in Virginia are:
Applied Machine Learning jobs near you
Infographic showing various Applied Machine Learning job openings in Virginia as of August 2026, with employment types broken down into 1% As Needed, 76% Full Time, 20% Part Time, 1% Temporary, and 2% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $42,218 per year, or $20.3 per hour.

Senior AI Security Software Engineer

Carnegie Mellon University

Arlington, VA • On-site

$131K - $180K/yr

Full-time

Re-posted 23 days ago

Carnegie Mellon University rating

8.6

Company rating: 8.6 out of 10

Based on 24 frontline employees who took The Breakroom Quiz

67th of 617 rated colleges and universities

Job description

Job Summary:
Carnegie Mellon University is seeking a Senior AI Security Software Engineer to join the CERT Division of the Software Engineering Institute (SEI). This role involves researching and developing AI security tactics and solutions, collaborating with researchers to address AI security challenges and contributing to national security through innovative technologies.
Responsibilities:
• Develop machine learning–based prototypes, tools, and systems for AI security applications, demonstrating strong expertise in ML development and deployment
• Collaborate with researchers and stakeholders to design and execute experimental AI security solutions, communicating effectively across technical and non-technical audiences
• Apply software engineering best practices to build scalable, maintainable systems, grounded design principles
• Process and analyze large, diverse cybersecurity datasets (e.g., malware, NetFlow, incident data), using strong analytical and problem-solving skills
• Support AI red teaming and adversarial machine learning initiatives, applying an innovative and research-driven mindset
• Translate research concepts into practical, operational capabilities, with the ability to work independently and as part of a collaborative team
Qualifications:
Required:
• BS in computer science, machine learning, cybersecurity, statistics, or related discipline with ten (10) years of experience; OR MS in the same fields with eight (8) years of experience; OR PhD in the same fields with five (5) years of experience
• Understanding of software engineering principles and system design
• Experience with containerization and microservices architectures
• Travel to various locations to support the SEI’s overall mission. This includes within the SEI and CMU community, sponsor sites, conferences, and offsite meetings on occasion (5%)
• You will be subject to a background check and will need to obtain and maintain a Department of War (DoW) security clearance
• Develop machine learning–based prototypes, tools, and systems for AI security applications, demonstrating strong expertise in ML development and deployment
• Collaborate with researchers and stakeholders to design and execute experimental AI security solutions, communicating effectively across technical and non-technical audiences
• Apply software engineering best practices to build scalable, maintainable systems, grounded design principles
• Process and analyze large, diverse cybersecurity datasets (e.g., malware, NetFlow, incident data), using strong analytical and problem-solving skills
• Support AI red teaming and adversarial machine learning initiatives, applying an innovative and research-driven mindset
• Translate research concepts into practical, operational capabilities, with the ability to work independently and as part of a collaborative team
Preferred:
• Experience applying statistical modeling and advanced data analytics techniques
• Background in developing AI/ML solutions in real-world settings
• Familiarity with applied machine learning domains (e.g., natural language processing, computer vision, autonomy, audio analysis)
• Experience and knowledge in cybersecurity best practices
• Demonstrated ability to quickly learn and adapt to new technologies and domains
Company:
Carnegie Mellon University is a research university offering programs and research across engineering, science, arts, and business. Founded in 1900, the company is headquartered in Pittsburgh, USA, with a team of 5001-10000 employees. The company is currently Late Stage.

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