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Internship Applied Scientist Machine Learning information

What does an internship applied scientist in machine learning do?

An Internship Applied Scientist in Machine Learning works on real-world projects involving the design, development, and evaluation of machine learning models and algorithms. Their responsibilities typically include data analysis, building predictive models, experimenting with new techniques, and collaborating with engineers and researchers to solve complex problems. Interns gain hands-on experience with tools like Python, TensorFlow, or PyTorch, and contribute to advancing the company's AI capabilities. The role requires a strong foundation in mathematics, statistics, and computer science, as well as the ability to communicate findings to both technical and non-technical stakeholders.

What types of projects do internship applied scientists in machine learning typically work on, and how do they contribute to the team's goals?

Internship Applied Scientists in Machine Learning often collaborate with multidisciplinary teams to tackle real-world problems using data-driven approaches. Typical projects might include developing and fine-tuning machine learning models, conducting experiments to validate hypotheses, or assisting in the deployment of algorithms into production systems. Interns are expected to contribute fresh perspectives, help with data preprocessing, and perform thorough model evaluations. Through these projects, interns gain hands-on experience while directly supporting the team's research and product development objectives.

What are the key skills and qualifications needed to thrive as an internship applied scientist in machine learning, and why are they important?

To thrive as an Internship Applied Scientist in Machine Learning, you need a solid background in mathematics, statistics, and computer science, often supported by coursework or research experience in machine learning and data analysis. Familiarity with tools such as Python, TensorFlow, PyTorch, and experience working with large datasets are highly valued, along with knowledge of version control systems like Git. Strong problem-solving skills, curiosity, and the ability to communicate complex concepts clearly set top candidates apart. These competencies are crucial for effectively designing, implementing, and presenting machine learning solutions that address real-world challenges.

What is the difference between Internship Applied Scientist Machine Learning vs Internship Data Scientist?

AspectInternship Applied Scientist Machine LearningInternship Data Scientist
Required CredentialsRelevant degrees in Computer Science, Data Science, or related fields; knowledge of ML frameworksDegrees in Statistics, Data Science, or related fields; strong analytical skills
Work EnvironmentResearch and development teams, focus on ML model developmentBusiness teams, focus on data analysis and insights
Employer & Industry UsageTech companies, AI-focused organizationsVarious industries including tech, finance, healthcare
Comparison Search IntentUnderstanding roles in ML research and developmentUnderstanding data analysis and business insights roles

Internship Applied Scientist Machine Learning roles focus on developing and applying machine learning models, often in research settings. In contrast, Internship Data Scientist positions emphasize analyzing data to generate insights for business decisions. Both roles require strong analytical skills and relevant educational backgrounds, but they differ in their primary focus and work environment.

What are the most commonly searched types of Applied Scientist Machine Learning jobs in Maryland?

The most popular types of Applied Scientist Machine Learning jobs in Maryland are:

What are popular job titles related to Internship Applied Scientist Machine Learning jobs in Maryland?

For Internship Applied Scientist Machine Learning jobs in Maryland, the most frequently searched job titles are:

What job categories do people searching Internship Applied Scientist Machine Learning jobs in Maryland look for?

The top searched job categories for Internship Applied Scientist Machine Learning jobs in Maryland are:

Internship Applied Scientist Machine Learning jobs near you
Infographic showing various Internship Applied Scientist Machine Learning job openings in Maryland as of August 2026, with employment types broken down into 1% As Needed, 72% Full Time, 23% Part Time, 1% Temporary, and 3% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution.

NLP & AI Applied Machine Learning Engineer

AIToolboard

Bethesda, MD • On-site

$110 - $160/hr

Other

Posted 25 days ago

Job description

Jobs / NLP & AI Applied Machine Learning Engineer

NLP & AI Applied Machine Learning Engineer

Full-time

About the Role

DescriptionThe Government Health and Safety Solutions Operation is on the lookout for a talented Applied Machine Learning Engineer to join our team.This position requires being onsite in Bethesda, MD (with some remote opportunities).ResponsibilitiesDesign, develop, and maintain innovative AI/ML solutions that enhance NIH grant application intake, peer review processes, and analytical insights. Utilize NLP and machine learning techniques (including embeddings, classification, clustering, and similarity analysis) for tasks such as reviewer-application matching and document analysis. Construct, evaluate, and continually improve machine learning models leveraging both structured and unstructured data, including text and images. Design and implement efficient end-to-end ML pipelines encompassing data ingestion, preprocessing, feature generation, model execution, evaluation, and output delivery. Debug, test, and optimize ML pipelines to ensure reliable, consistent, and reproducible outcomes. Refine and enhance code for improved performance, scalability, and maintainability. Work with complex datasets, implementing data validation, quality checks, and preprocessing workflows. Conduct experiments to assess model performance, analyze findings, and adjust strategies based on quantitative and qualitative results. Collaborate with multidisciplinary teams to translate business needs into effective AI/ML solutions. Ensure transparency and reproducibility through meticulous documentation and structured workflows. Communicate technical methods, results, and limitations clearly to both technical and non-technical stakeholders. Keep abreast of advancements in applied AI/ML, particularly in NLP, embeddings, and generative AI, and assess their relevance to NIH projects.Required QualificationsMaster’s degree in data science, Computer Science, Computational Linguistics, or a related field (or equivalent experience). 3-5 years of relevant experience in applied machine learning, data science, or a related field. Strong programming skills in Python (preferred) and/or R. Proven experience delivering comprehensive ML solutions, including model development, evaluation, and pipeline implementation. Hands‑on experience developing and applying machine learning models. Familiarity with NLP techniques such as text classification and semantic similarity. Experience working in cloud or shared computing environments (e.g., Azure, Biowulf). Proficiency in building and maintaining data processing or ML pipelines. Experience cleaning, preprocessing, and engineering features from real‑world datasets. Ability to debug, test, and improve complex code and workflows. Knowledge of at least one modern ML framework (e.g., PyTorch, TensorFlow, scikit‑learn). Strong analytical capabilities and problem‑solving skills. Excellent communication skills for conveying technical concepts to diverse audiences.Preferred QualificationsExperience with transformer models or large language models in text analysis or document processing. Familiarity with reviewer matching, recommendation systems, or document similarity challenges. Knowledge of distributed data processing tools (e.g., Spark, Dask). Experience with experiment tracking and reproducible workflows (e.g., MLflow). Familiarity with NIH data systems or scientific research datasets. Experience with medical or scientific imaging and AI model evaluation. Understanding of evaluation metrics (e.g., accuracy, precision, recall) and model robustness.If you’re seeking a dynamic role where you can make an impact, we want to hear from you! At Leidos, we value innovation and strive to push boundaries in mission‑focused initiatives.

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