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Machine Learning Engineer Intern Jobs in Norfolk, VA

Bespoke Labs

Machine Learning Engineer

Norfolk, VA

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

Bespoke Labs

Machine Learning Engineer

Hampton, VA · On-site

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

Bespoke Labs

Machine Learning Engineer

Hampton, VA

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

Bespoke Labs

Machine Learning Engineer

Chesapeake, VA · On-site

* Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch * Build and maintain the infrastructure around RL training: rollout collection ...

Defianx

AI / Machine Learning Engineer

Norfolk, VA · On-site

Position Summary The AI/ML Engineer will design, build, integrate, evaluate, and maintain artificial-intelligence and machine-learning capabilities supporting NATO operational and capability ...

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Showing results 21-40

All JobsMachine Learning Engineer Intern JobsMachine Learning Engineer Intern Jobs in Norfolk, VA

Machine Learning Engineer Intern information

See Norfolk, VA salary details

$24.7K

$41.2K

$85.1K

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

As of Sep 14, 2026, the average yearly pay for machine learning engineer intern in Norfolk, VA is $41,201.00, according to ZipRecruiter salary data. Most workers in this role earn between $31,400.00 and $44,500.00 per year, depending on experience, location, and employer.

What is a machine learning engineer intern?

A Machine Learning Engineer Intern is a temporary, entry-level role where individuals work with data scientists and engineers to develop, test, and optimize machine learning models. Interns typically assist in data preprocessing, feature engineering, model training, and evaluation. They may also work on improving existing algorithms, implementing research papers, or deploying models into production. This role provides hands-on experience with machine learning frameworks such as TensorFlow and PyTorch, as well as coding in Python and working with large datasets. The internship helps build practical skills and industry experience in artificial intelligence and data science.

What do machine learning engineer interns do?

Machine Learning Engineer Interns are often involved in data preparation, feature engineering, model development, and performance evaluation under the guidance of senior engineers or data scientists. You may help implement and test machine learning algorithms, assist in cleaning and visualizing datasets, and contribute to code reviews or research tasks. Interns frequently collaborate with cross-functional teams, such as data scientists, software engineers, and product managers, to solve real-world problems and support ongoing projects. This hands-on experience provides valuable insights into the practical application of machine learning in a professional setting.

What skills and qualifications are needed to thrive as a machine learning engineer intern?

To thrive as a Machine Learning Engineer Intern, you need a solid understanding of programming languages such as Python, knowledge of machine learning algorithms, and experience with data analysis, typically supported by coursework in computer science or related fields. Familiarity with tools like TensorFlow, PyTorch, scikit-learn, and version control systems such as Git is often required. Strong problem-solving abilities, attention to detail, and effective communication are valuable soft skills in this role. These competencies enable interns to contribute meaningfully to projects, collaborate efficiently with teams, and adapt in a fast-paced, tech-driven environment.

What are the most commonly searched types of Machine Learning Engineer jobs in Norfolk, VA?

The most popular types of Machine Learning Engineer jobs in Norfolk, VA are:

What are popular job titles related to Machine Learning Engineer Intern jobs in Norfolk, VA?

For Machine Learning Engineer Intern jobs in Norfolk, VA, the most frequently searched job titles are:

What job categories do people searching Machine Learning Engineer Intern jobs in Norfolk, VA look for?

The top searched job categories for Machine Learning Engineer Intern jobs in Norfolk, VA are:

What cities near Norfolk, VA are hiring for Machine Learning Engineer Intern jobs?

Cities near Norfolk, VA with the most Machine Learning Engineer Intern job openings:

Machine Learning Engineer Intern jobs near you
Infographic showing various Machine Learning Engineer Intern job openings in Norfolk, VA as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 75% Full Time, 21% Part Time, and 2% Contract. Highlights an 73% Physical, 2% Hybrid, and 25% Remote job distribution, with an average salary of $41,201 per year, or $19.8 per hour.

Machine Learning Engineer

Norfolk, VA

Bespoke Labs
See more jobs at Bespoke Labsexternal

Full-time

Re-posted 29 days ago

Job description

  • Own model training and post-training pipelines end to end: SFT, RLHF, PPO, DPO, and reward model training in PyTorch

  • Build and maintain the infrastructure around RL training: rollout collection, data curation, reward model serving, and experiment orchestration

  • Run and scale training experiments on cloud or HPC (AWS, GCP, SLURM, Ray), and debug throughput, stability, and convergence issues

  • Build evaluation harnesses and benchmark infrastructure, with held-out sets and contamination controls, so results are trustworthy

  • Read eval signal and training curves to determine whether a change actually helped, and feed findings back to the research and environment teams

  • Integrate RL environments into the training stack, working with environment authors on interfaces, reward plumbing, and agent loop mechanics

  • Implement methods from recent ML papers quickly and turn them into production-grade systems

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