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Machine Learning Research Intern Jobs in Virginia

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Machine Learning Research Intern information

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How much do machine learning research intern jobs pay per hour?

As of Aug 10, 2026, the average hourly pay for machine learning research intern in Virginia is $22.16, according to ZipRecruiter salary data. Most workers in this role earn between $14.04 and $25.72 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a machine learning research intern?

To thrive as a Machine Learning Research Intern, you need a strong foundation in mathematics, statistics, programming (especially Python), and an understanding of machine learning algorithms, typically supported by ongoing or completed studies in computer science or related fields. Familiarity with technical tools such as TensorFlow, PyTorch, scikit-learn, and experience with data analysis libraries are commonly required. Curiosity, problem-solving ability, and effective communication skills help interns stand out by enabling them to collaborate, share insights, and adapt to new research challenges. These skills ensure interns can contribute meaningfully to research projects, quickly learn new techniques, and effectively communicate their findings.

What are some typical challenges faced by machine learning research interns during their projects?

Machine Learning Research Interns often encounter challenges such as dealing with limited or messy datasets, tuning complex model architectures, and balancing innovative research with practical implementation. Additionally, they may need to quickly familiarize themselves with unfamiliar frameworks or tools and effectively communicate technical findings to both technical and non-technical team members. Successfully navigating these challenges can provide valuable learning experiences and help interns build strong problem-solving skills for future roles.

What does a machine learning research intern do?

A Machine Learning Research Intern assists in the development, implementation, and evaluation of machine learning models and algorithms under the supervision of experienced researchers. They often preprocess data, run experiments, analyze results, and contribute to research papers or technical reports. Interns also stay up to date with the latest advancements in machine learning, participate in team meetings, and sometimes help in coding or optimizing existing models. This role provides hands-on experience in applying theoretical knowledge to real-world problems and prepares interns for careers in AI research or development.
What cities in Virginia are hiring for Machine Learning Research Intern jobs? Cities in Virginia with the most Machine Learning Research Intern job openings:
Infographic showing various Machine Learning Research Intern 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 $46,093 per year, or $22.2 per hour.

Senior Machine Learning Research Scientist - Frontier Lab

Carnegie Mellon University

Arlington, VA • On-site

$113K - $144K/yr

Full-time

Re-posted 21 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 a leading institution in applied artificial intelligence research. The Senior Machine Learning Research Scientist in the Frontier Lab will conduct applied research and develop prototypes for government missions, collaborating across research and engineering disciplines while providing technical leadership.
Responsibilities:
• Execute work within the operational context—understanding users, workflows, constraints, success criteria, and outcomes—so technical decisions are grounded in real mission needs.
• Lead technical execution by defining technical tasking, sequencing work into realistic milestones, maintaining delivery quality, and delegating appropriately across the team.
• Design and run studies, build convincing prototypes and reference implementations, and produce evidence-backed insights that can be matured and transitioned into operational settings.
• Establish credible evaluation strategies and test pipelines that assess performance, robustness, reliability, and trustworthiness in mission-representative scenarios.
• Serve as the primary technical interface when appropriate; translate mission goals into measurable technical outcomes; communicate progress, decisions, and risks clearly to stakeholders.
• Proactively mentor junior staff and teammates, raising the bar for research rigor, engineering practice, and delivery habits across project teams.
• Maintain strong awareness of frontier developments aligned to the Frontier Lab, share insights with the lab, and help shape research directions and future work selection.
• Manage multiple priorities effectively, sustain steady execution cadence, and resolve blockers with minimal oversight.
• Build a strong research culture through internal talks, reading groups, and workshops; and engage with external AI/ML communities (professional societies, consortiums, working groups, and conferences) to strengthen collaboration pathways and keep the lab connected to emerging practice.
Qualifications:
Required:
• BS in Computer Science, Electrical Engineering, Statistics, or related field with 10 years of relevant experience; OR MS with 8 years of relevant experience; OR PhD with 5 years of relevant experience.
• Deep expertise in one or more Frontier Lab-aligned areas (agentic systems, LLM reliability/evaluation, CV evaluation, robustness/assurance, TEVV pipelines, multimodal learning, edge ML).
• Strong engineering capability – can build and maintain high-quality prototypes, evaluation infrastructure, and repeatable experimentation workflows.
• Strong written and verbal communication skills; able to represent technical work credibly to senior stakeholders.
• Demonstrated ability to lead technical workstreams and coordinate multi-person execution.
• Technical judgment: Makes sound architectural and methodological decisions; balances ambition with mission constraints.
• Customer translation: Converts mission needs into tractable technical plans, measurable success criteria, and credible evaluation evidence.
• Scientific leadership: Maintains rigor; identifies flawed assumptions; improves evaluation quality and research practices.
• Mentorship & influence: Elevates team performance through hands-on guidance and strong technical standards.
• Initiative: Proactively identifies risks/opportunities, proposes new work, and creates alignment without directive management.
• Self-direction and time management: Plans work effectively under ambiguity, maintains execution cadence, and escalates risks early.
• Flexible to travel to SEI offices in Pittsburgh, PA and Washington, DC / Arlington, VA, sponsor sites, conferences, and offsite meetings (~10% travel).
• You must be able and willing to work onsite at an SEI office in Pittsburgh, PA or Arlington, VA 5 days per week.
• You will be subject to a background investigation and must be eligible to obtain and maintain a Department of War security clearance.
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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