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Machine Learning Engineer Intern Jobs in Philadelphia, PA

BE Group

Senior Machine Learning Engineer

Malvern, PA

$120K - $158K/yr

We are assisting our client in hiring for a Senior Machine Learning Engineer. Our client is an established SaaS company serving banks, credit unions, and fintechs. Their cloud-based platform helps ...

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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.

Vanguard Group

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.

Bespoke Labs

Machine Learning Engineer

Trenton, NJ · On-site

Must-Have Skills 3+ years of ML engineering experience -- model training, fine-tuning, or post-training pipelines in research or production Strong Python and deep learning proficiency (PyTorch ...

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

Machine Learning Engineer Intern information

See Philadelphia, PA salary details

$25.7K

$43K

$88.8K

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

As of Jul 30, 2026, the average yearly pay for machine learning engineer 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 types of projects and tasks do Machine Learning Engineer Interns typically work on?

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 is a Machine Learning Engineer Intern job?

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 are the key skills and qualifications needed to thrive in the Machine Learning Engineer Intern position, and why are they important?

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 Philadelphia, PA? The most popular types of Machine Learning Engineer jobs in Philadelphia, PA are:
What are popular job titles related to Machine Learning Engineer Intern jobs in Philadelphia, PA? For Machine Learning Engineer Intern jobs in Philadelphia, PA, the most frequently searched job titles are:
What job categories do people searching Machine Learning Engineer Intern jobs in Philadelphia, PA look for? The top searched job categories for Machine Learning Engineer Intern jobs in Philadelphia, PA are:
What cities near Philadelphia, PA are hiring for Machine Learning Engineer Intern jobs? Cities near Philadelphia, PA 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 Philadelphia, PA as of July 2026, with employment types broken down into 92% Full Time, 5% Part Time, and 3% Contract. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution, with an average salary of $42,971 per year, or $20.7 per hour.

Machine Learning Engineer - Databricks

Aptino

Malvern, PA • On-site

Other

Posted 2 days ago

New

Job description

Role: Machine Learning Engineer – Databricks

Location: Malvern, PA (Hybrid – 3 days/week onsite)
Duration: 12 Months

Position Overview:

We are seeking an experienced Machine Learning Engineer with strong expertise in the Databricks Lakehouse Platform to develop, deploy, and optimize scalable AI/ML solutions. The ideal candidate will have hands-on experience building production-ready machine learning models, implementing MLOps best practices, and designing end-to-end ML pipelines using Databricks, Spark, and AWS cloud services.

Key Responsibilities:
  • Design, develop, and deploy scalable machine learning models using the Databricks Machine Learning platform.
  • Build and optimize end-to-end ML pipelines, including data ingestion, feature engineering, model training, validation, deployment, and monitoring.
  • Utilize MLflow for experiment tracking, model versioning, lifecycle management, and production deployments.
  • Develop high-performance data processing pipelines using PySpark, Apache Spark, and SQL to support large-scale analytics and machine learning workloads.
  • Build and maintain production-grade applications and ML workflows on AWS, leveraging services such as Lambda, S3, Glue, ECS/EKS, Step Functions, SageMaker, and Bedrock.
  • Implement feature engineering, model evaluation, hyperparameter optimization, and performance tuning to improve model accuracy and scalability.
  • Collaborate with data engineers, data scientists, and business stakeholders to translate business requirements into production-ready ML solutions.
  • Establish MLOps best practices, CI/CD processes, monitoring strategies, and governance standards for machine learning deployments.
  • Optimize data architecture and machine learning workflows using Databricks Lakehouse, Delta Lake, and Unity Catalog.
  • Contribute to AI innovation initiatives by evaluating emerging technologies, Generative AI use cases, and modern machine learning frameworks.
Required Qualifications:
  • 5+ years of experience in Machine Learning, Artificial Intelligence, or Data Science engineering.
  • Strong hands-on expertise with Databricks Machine Learning and the Databricks ecosystem.
  • Proven experience using MLflow for experiment management, model registry, and deployment.
  • Strong programming skills in Python, PySpark, Apache Spark, and SQL.
  • Experience developing supervised and unsupervised machine learning models for enterprise applications.
  • Hands-on experience building, deploying, and supporting production-grade ML pipelines.
  • Solid understanding of feature engineering, model validation, hyperparameter tuning, and model performance optimization.
  • Experience developing cloud-native AI/ML solutions on AWS.
  • Experience building scalable data pipelines using Spark, Glue, Airflow, dbt, or similar orchestration tools.
  • Strong analytical, troubleshooting, and problem-solving skills with experience handling large datasets.
Preferred Qualifications:
  • Experience designing solutions on the Databricks Lakehouse Architecture.
  • Knowledge of Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), or AI-powered applications.
  • Experience with orchestration frameworks such as Apache Airflow or Azure Data Factory.
  • Familiarity with Docker, Kubernetes, CI/CD, and modern DevOps practices.
  • Hands-on experience with Delta Lake, Unity Catalog, and enterprise data governance.
  • Databricks certification is an added advantage.

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