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Machine Learning Research Engineer Jobs in Virginia

Machine Learning Engineer LOCATION Tysons, VA 22182 CLEARANCE TS/SCI Full Poly (Please note this ... Engineer, Research Scientist, Data Engineer, NLP Engineer, Computer Vision Engineer, AI/ML ...

Machine Learning Engineer LOCATION Chantilly, VA 20151 CLEARANCE TS/SCI Full Poly (Please note this ... Engineer, Research Scientist, Data Engineer, NLP Engineer, Computer Vision Engineer, AI/ML ...

Machine Learning Engineer LOCATION Reston, VA 20190 CLEARANCE TS/SCI Full Poly (Please note this ... Engineer, Research Scientist, Data Engineer, NLP Engineer, Computer Vision Engineer, AI/ML ...

Machine Learning Researcher

Broadway, VA ยท On-site

$154K - $213K/yr

Morgan Stanley's Machine Learning Research Department is responsible for working across the Firms many business units and technology teams to solve mission-critical problems. We are a highly ...

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Machine Learning Research Engineer information

See Virginia salary details

$36.7K

$105.1K

$141.3K

How much do machine learning research engineer jobs pay per year?

As of Jul 12, 2026, the average yearly pay for machine learning research engineer in Virginia is $105,103.00, according to ZipRecruiter salary data. Most workers in this role earn between $103,100.00 and $103,100.00 per year, depending on experience, location, and employer.

What does a machine learning research engineer do?

A machine learning research engineer develops and improves algorithms and models that enable computers to learn from data. They often work on creating new techniques, testing prototypes, and publishing findings, using tools like Python, TensorFlow, or PyTorch. Their work supports advancing AI capabilities and typically requires strong programming, statistical, and mathematical skills.

How much do ML research engineers make?

Machine Learning Research Engineers typically earn between $90,000 and $150,000 annually, with salaries increasing based on experience, education, and location. Senior roles or those with specialized skills in deep learning, NLP, or computer vision can earn over $200,000. Compensation often includes benefits such as bonuses, stock options, and professional development opportunities.

What does a Machine Learning Research Engineer do?

A Machine Learning Research Engineer develops and improves machine learning models, conducts research to advance AI techniques, and implements scalable algorithms. They work at the intersection of applied research and engineering, leveraging mathematical and statistical methods to optimize performance. Their role involves experimenting with new architectures, analyzing large datasets, and collaborating with data scientists and software engineers to deploy models into production.

What are some common challenges faced by Machine Learning Research Engineers in their daily work?

Machine Learning Research Engineers often encounter challenges such as sourcing and preparing large, high-quality datasets, tuning complex model architectures, and ensuring reproducibility of experimental results. They work closely with cross-functional teams, including data scientists and software engineers, to deploy models in production environments and must frequently adapt to rapidly evolving research. Keeping up with the latest scientific literature and integrating new algorithms into ongoing projects can be demanding but is also rewarding. This collaborative, fast-paced environment provides constant opportunities for learning and professional development.

What are the key skills and qualifications needed to thrive in the Machine Learning Research Engineer position, and why are they important?

A Machine Learning Research Engineer typically needs a strong background in computer science, mathematics, and statistics, often with a graduate degree in a related field. Proficiency in programming languages such as Python or C++, experience with machine learning frameworks like TensorFlow or PyTorch, and familiarity with tools for data analysis are crucial, along with relevant certifications being a plus. Strong problem-solving skills, collaboration, and effective communication help drive innovative research and facilitate teamwork. These competencies are essential for developing advanced machine learning models, staying current with evolving technologies, and effectively translating research into real-world applications.

What engineers make $500,000?

Senior machine learning research engineers with extensive experience, advanced skills in deep learning and data science, and often working in high-demand industries or companies can earn $500,000 or more annually. Compensation typically includes base salary, bonuses, and stock options, especially in tech giants or startups with significant funding.

What engineers make $300,000 a year?

Senior machine learning research engineers with extensive experience, advanced skills in deep learning and data science, and often a strong publication record can earn $300,000 or more annually. Compensation varies based on industry, location, company size, and individual expertise, with roles in tech giants and finance firms typically offering higher salaries.
What job categories do people searching Machine Learning Research Engineer jobs in Virginia look for? The top searched job categories for Machine Learning Research Engineer jobs in Virginia are:
Senior Data Scientist / AI Machine Learning Research Engineer

Senior Data Scientist / AI Machine Learning Research Engineer

CACI International, Inc.

Sterling, VA โ€ข On-site

$113K - $237K/yr

Full-time

Medical, Retirement, PTO

Posted 13 days ago


Job description

Job Title: Senior Data Scientist / AI Machine Learning Research Engineer
Job Category: Science
Time Type: Full time
Minimum Clearance Required to Start: TS/SCI
Employee Type: Regular
Percentage of Travel Required: Up to 10%
Type of Travel: Continental US
* * *
The Opportunity:
CACI has an exciting new opportunity for a Senior AI and Machine Learning Research Engineer. Apply machine learning, statistics, to develop algorithms to solve challenging problems, signal processing, and computer networking domains. In this role, you'll leverage your strong foundation in machine learning, data science, and signal processing to solve complex challenges in the RF domain.
Responsibilities:
1. Strong mathematical foundation in statistics, linear algebra, and calculus with demonstrated ability to understand and implement machine learning algorithms from first principles rather than solely relying on pre-built libraries.
2. Proficiency in designing and building data pipelines, including experience with ETL processes, data warehousing solutions, and optimizing workflows for large-scale data processing.
3. Hands-on experience with cloud-based infrastructure (e.g., AWS, Azure, GCP) for deploying ML solutions, including containerization, orchestration, and CI/CD pipelines for model deployment.
4. Programming expertise in Python and SQL, with experience using data engineering frameworks (e.g., Spark, Airflow) and ML libraries (e.g., TensorFlow, PyTorch, scikit-learn).
5. Demonstrated experience in establishing ML governance practices, including version control for datasets and models, experiment tracking, model monitoring, and implementing reproducible research principles.
Qualifications:
Required:
  • Master's degree in quantitative field with mathematical underpinnings and at least 7 years' experience.
  • Experience developing models,.
  • Strong background in machine learning, mathematics and statistics.
  • Comfortable using Linux operating systems and commonly used Linux utilities.
  • Must be a US Citizen with the ability to obtain, maintain and/or transfer the required security clearance as dictated by the contract
  • Must have active Top Secret Clearance with the ability to obtain SCI with Polygraph

Desired:
  • Ph.D. in computer science, computer engineering, or machine learning, Statistics, applied mathematics or Physics.
  • Experience applying machine learning to signal processing and/or other time-series data analysis applications.
  • Knowledge of or experience with information theory, probability theory, parametric and non-parametric statistical tests.
  • Familiarity with concepts and techniques associated with adversarial AI and AI/ML assurance.
  • Active Top Secret/SCI clearance preferred.

What You Can Expect:
A culture of integrity.
At CACI, we place character and innovation at the center of everything we do. As a valued team member, you'll be part of a high-performing group dedicated to our customer's missions and driven by a higher purpose - to ensure the safety of our nation.
An environment of trust.
CACI values the unique contributions that every employee brings to our company and our customers - every day. You'll have the autonomy to take the time you need through a unique flexible time off benefit and have access to robust learning resources to make your ambitions a reality.
A focus on continuous growth.
Together, we will advance our nation's most critical missions, build on our lengthy track record of business success, and find opportunities to break new ground - in your career and in our legacy.
Pay Range:
There are a host of factors that can influence final salary including, but not limited to, geographic location, Federal Government contract labor categories and contract wage rates, relevant prior work experience, specific skills and competencies, education, and certifications. Our employees value the flexibility at CACI that allows them to balance quality work and their personal lives. We offer competitive compensation, benefits and learning and development opportunities. Our broad and competitive mix of benefits options is designed to support and protect employees and their families. At CACI, you will receive comprehensive benefits such as; healthcare, wellness, financial, retirement, family support, continuing education, and time off benefits.
The proposed salary range for this position is:
$113,200 - $237,800
CACI is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, pregnancy, sexual orientation, age, national origin, disability, status as a protected veteran, or any other protected characteristic.