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Machine Learning Developer Intern Jobs in Philadelphia, PA

Vanguard

Senior Machine Learning Engineer

Malvern, PA · On-site

$102K - $140K/yr

This role partners closely with quantitative researchers, data scientists, and investment teams to engineer, deploy, and operate production-grade machine learning models that drive research ...

New

Vangard, Inc.

Senior Machine Learning Engineer

Malvern, PA · On-site

$102K - $140K/yr

Design, build, and maintain end-to-end machine learning pipelines from research through production deployment. * Engineer scalable training, inference, and retraining workflows using AWS SageMaker.

asrcfh

Expert Machine Learning Engineer

Moorestown, NJ · On-site

We are seeking an Expert Machine Learning Engineer to join our team in Moorestown, NJ. Responsibilities: • Develops, researches, and applies machine learning, deep learning, visual artificial ...

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Showing results 21-40

All JobsMachine Learning Developer Intern JobsMachine Learning Developer Intern Jobs in Philadelphia, PA

Machine Learning Developer Intern information

See Philadelphia, PA salary details

$25.7K

$43K

$88.8K

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

As of Sep 11, 2026, the average yearly pay for machine learning developer intern in Philadelphia, PA is $42,971.00, according to ZipRecruiter salary data. Most workers in this role earn between $32,800.00 and $46,400.00 per year, depending on experience, location, and employer.

What does a machine learning developer intern do?

A Machine Learning Developer Intern assists with developing, testing, and implementing machine learning models and algorithms under the guidance of experienced engineers or data scientists. Their tasks may include data preprocessing, model training, evaluating model performance, and helping deploy models into production environments. Interns often collaborate with team members to solve real-world problems using machine learning techniques and may also assist in researching new methodologies or optimizing existing solutions. This role provides hands-on experience in coding, data analysis, and applying theoretical concepts to practical scenarios.

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

To thrive as a Machine Learning Developer Intern, you need a solid understanding of programming (especially Python), statistics, and machine learning concepts, often supported by coursework or relevant project experience. Familiarity with ML frameworks like TensorFlow or PyTorch, and tools such as Jupyter Notebooks and version control systems like Git, is typically expected. Strong analytical thinking, eagerness to learn, and effective communication help interns contribute to team projects and adapt quickly. These skills are essential for solving real-world problems, collaborating with teams, and building a foundation for a successful career in machine learning.

How do machine learning developer interns typically collaborate with data scientists and engineers during their internship?

Machine Learning Developer Interns often work closely with data scientists to understand the problem domain, gather relevant datasets, and select appropriate models. They also collaborate with software engineers to integrate machine learning solutions into existing systems, ensuring scalability and performance. Regular communication through stand-up meetings, code reviews, and collaborative platforms is common, allowing interns to learn best practices and receive feedback on their work. This teamwork not only enhances technical skills but also provides valuable exposure to real-world deployment and project lifecycle management.

What is the difference between Machine Learning Developer Intern vs Data Scientist Intern?

AspectMachine Learning Developer InternData Scientist Intern
Required CredentialsTypically pursuing or recently completed a degree in Computer Science, Data Science, or related fields; knowledge of programming languages like Python or JavaSimilar educational background; strong skills in statistics, programming, and data analysis
Work EnvironmentHands-on experience with ML models, algorithms, and software development in tech or research settingsData analysis, visualization, and interpretation in business or research contexts
Employer & Industry UsageTech companies, startups, research labs focusing on AI/ML projectsBusiness, finance, healthcare, and research organizations analyzing large datasets

Both roles involve working with data and programming, but Machine Learning Developer Interns focus more on building and deploying ML models, while Data Scientist Interns emphasize data analysis and insights. The roles often overlap, especially in tech environments, but their core tasks differ slightly.

Machine Learning Developer Intern jobs near you

Principal Machine Learning Engineer

Philadelphia, PA • On-site

Delan Associates, Inc.
Engineering Professional Services • 51 - 200 employees
Learn more about Delan Associates, Inc.external

Contractor

Re-posted 27 days ago

Job description

Principal Machine Learning Engineer to serve as a hands-on technical leader for machine learning, predictive modeling, scoring, decisioning, and applied AI initiatives. This role will primarily focus on building, validating, deploying, and improving machine learning models, while also bringing principal-level judgment to problem definition, model design, stakeholder engagement, and production readiness.
Hands-On Model Development
Build, test, validate, and improve machine learning models for scoring, prediction, prioritization, risk detection, engagement, intervention targeting, and decision support.
Perform exploratory data analysis, data quality assessment, feature engineering, model training, model selection, and performance evaluation.
Develop practical ML models that balance predictive performance, explainability, stability, maintainability, and business usefulness.
Work with structured, semi-structured, and operational data to create model-ready datasets and reusable features.
Use tools such as Python, SQL, Spark, Databricks, MLflow, scikit-learn, XGBoost, or similar platforms and libraries.
Move quickly from data exploration to prototype to validated model to production-ready capability.
Required Qualifications
Professional experience in machine learning, data science, software engineering, analytics engineering, applied AI, or related technical fields.
5+ years of hands-on machine learning model development experience, including feature engineering, model training, validation, evaluation, and iteration.
3+ years of experience deploying, operationalizing, or supporting models in production or business-critical environments.
Strong hands-on experience with Python and SQL.
Experience with modern ML and data platforms such as Databricks, Spark, MLflow, Snowflake, Azure, AWS, or similar technologies.
Strong understanding of model evaluation, calibration, thresholding, score interpretation, monitoring, drift, retraining, and production ML lifecycle management.
Experience translating ambiguous business problems into concrete ML designs, model requirements, validation plans, and measurable outcomes.
Ability to explain model behavior, model performance, assumptions, limitations, and tradeoffs to both technical and non-technical stakeholders.
Strong engineering discipline, including clean code, reproducibility, versioning, testing, documentation, and maintainability.
Ability to work independently as a senior hands-on contributor while also providing technical leadership and modeling judgment.
Scoring, Scorecards, and Transparent Models
Production ML and MLOps
Product and Rapid-Build Execution
Generative AI and AI Automation
Requirement Shaping and Stakeholder Partnership

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