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Machine Learning Research Engineer Jobs in Virginia

Machine Learning Engineer

Chantilly, VA · On-site

$120K - $180K/yr

Our machine learning teams bridge the gap between cutting-edge AI research and operational government missions. We are looking for engineers who can take machine learning and Computer Vision (CV ...

Machine Learning Engineer

Arlington, VA · Hybrid

$110K - $200K/yr

Kitware is a leader in advanced research and algorithm development in artificial intelligence (AI ... Machine learning experience using visual data * Understanding of a variety of machine learning ...

Machine Learning Engineer

Arlington, VA · Hybrid

$110K - $200K/yr

Kitware is a leader in advanced research and algorithm development in artificial intelligence (AI ... Machine learning experience using visual data * Understanding of a variety of machine learning ...

Machine Learning Engineer

Arlington, VA · On-site

$110K - $200K/yr

Kitware is a leader in advanced research and algorithm development in artificial intelligence (AI ... Machine learning experience using visual data * Understanding of a variety of machine learning ...

Machine Learning Engineer

Arlington, VA · On-site

$115K - $140K/yr

The Role As a Machine Learning Engineer, you will help develop and integrate cutting-edge AI/ML ... Working as part of a multidisciplinary team of researchers, engineers, and domain experts, you will ...

Machine Learning Engineer

Arlington, VA · On-site

$115K - $140K/yr

The Role As a Machine Learning Engineer, you will help develop and integrate cutting-edge AI/ML ... Working as part of a multidisciplinary team of researchers, engineers, and domain experts, you will ...

Engineer, Machine Learning

Arlington, VA · On-site

$157K - $185K/yr

Engineer, Machine Learning Located: Arlington Summary The Machine Learning Engineer will design, develop, and maintain the productionization of machine learning, deep learning, generative AI, large ...

Showing results 21-40

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 Sep 11, 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 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 the key skills and qualifications needed to thrive as a machine learning research engineer?

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 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.

Infographic showing various Machine Learning Research Engineer job openings in Virginia as of August 2026, with employment types broken down into 1% As Needed, 70% Full Time, 28% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $105,103 per year, or $50.5 per hour.

Talent Intelligence Research Engineer

Alexandria, VA

McChrystal Group
Business Management Consulting • 51 - 200 employees

$125K - $190K/yr

Full-time

Re-posted 28 days ago


Job description

Position Overview: 

Our consultants work side by side with client organizations to help them make smarter decisions about their people. The Talent Intelligence Research Engineer is the analytical engine behind that work. 

In this role, you will be embedded on client-facing consulting teams, working directly with clients to answer complex questions about their workforce and talent landscape. The data you work with spans publicly available talent and talent-adjacent data, as well as clients' own workforce data. 

The questions you'll tackle might look like: How much should we be paying for this role in this market? Where does the talent we need actually exist, and can we compete for it? How does our workforce compare to our competitors? What skills does our organization have today, and what are we missing? 

To answer those questions, you'll pull from a wide toolkit, writing code, scraping and acquiring data from public sources, applying natural language processing, machine learning, and AI techniques, and designing custom analytical approaches when no off-the-shelf solution exists. Every engagement is different, and the problems are genuinely novel. 

This is not a role that maintains systems or runs recurring reports. It is investigative and project-based by nature. You'll move from engagement to engagement, working alongside consultants and client business leaders to develop proprietary methodologies, build analytical capabilities that don't exist anywhere else, and deliver the data assets and insights that help clients make better decisions about their talent, workforce, organization, and leadership. 

Workforce & Talent Intelligence
  • Conduct research and analysis related to labor markets, compensation, talent availability, workforce composition, organizational structures, skills, and recruiting dynamics. 

  • Develop methodologies to estimate or infer workforce attributes that are not directly observable. 

  • Analyze talent pools, labor supply, competitive hiring environments, and organizational capabilities. 

  • Produce actionable talent intelligence for client engagements. 

  • Communicate findings, assumptions, confidence levels, and limitations to both technical and non-technical audiences. 

Research Engineering & Data Acquisition
  • Acquire data from public, commercial, and proprietary sources. 

  • Develop custom web scraping, extraction, and enrichment workflows to support research initiatives. 

  • Build one-off software tools and analytical applications required to answer specific business questions. 

  • Evaluate data quality, completeness, and reliability across multiple sources. 

  • Rapidly learn and apply new technologies, techniques, and datasets as project requirements evolve. 

Machine Learning, AI & Advanced Analytics
  • Apply machine learning, natural language processing, statistical methods, and generative AI techniques to solve talent intelligence problems. 

  • Develop similarity, matching, classification, clustering, ranking, and inference approaches when appropriate. 

  • Leverage large language models and modern AI tooling to accelerate research and insight generation. 

  • Design experiments and validation approaches to assess analytical accuracy and reliability. 

  • Translate analytical outputs into practical business recommendations. 

Consulting & Collaboration
  • Partner with consultants, researchers, and client-facing stakeholders to understand business challenges. 

  • Contribute to the development of proprietary talent intelligence methodologies and intellectual property. 

  • Support client engagements through research, analytical problem solving, and technical expertise. 

  • Present research findings and recommendations to clients and internal stakeholders. 

  • Share tools, approaches, and best practices across the organization. 

Qualifications:

Required Qualifications 

  • Bachelor's degree in Computer Science, Data Science, Statistics, Economics, Mathematics, Engineering, Social Sciences, or a related quantitative field. 

  • 4-6 years of relevant experience 

  • Experience conducting independent analytical or research-oriented projects. 

  • Strong programming skills, particularly in Python. 

  • Strong analytical reasoning and problem-solving abilities. 

  • Ability to work effectively in ambiguous environments with limited precedent or direction. 

  • Excellent written and verbal communication skills. 

  • Experience with labor market, workforce, recruiting, compensation, or organizational data. 

  • Experience working with large structured and unstructured datasets. 

  • Must be able to obtain and maintain a U.S. Government security clearance. 

Preferred Qualifications 

  • Experience with web scraping, data acquisition, and information extraction. 

  • Experience with machine learning, NLP, or AI-assisted analytics. 

  • Exposure to consulting, market intelligence, economic research, competitive intelligence, or workforce analytics environments. 

$125,000 - $190,000 a year
We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.
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