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Artificial Intelligence Machine Learning Engineer Jobs in Philadelphia, PA

BE Group

Senior Machine Learning Engineer

Malvern, PA

$120K - $158K/yr

This role is focused on building the predictive intelligence that becomes part of the company ... If you enjoy owning the entire machine learning lifecycle--from feature engineering and model ...

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SEI Investments

AI Cybersecurity Engineer

Oaks, PA

We are seeking an AI Cybersecurity Engineer to serve as a technical security lead and architect ... This strategic role combines deep expertise in artificial intelligence, machine learning, and ...

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All JobsArtificial Intelligence Machine Learning Engineer JobsArtificial Intelligence Machine Learning Engineer Jobs in Philadelphia, PA

Artificial Intelligence Machine Learning Engineer information

See Philadelphia, PA salary details

$30.1K

$123.1K

$184.9K

How much do artificial intelligence machine learning engineer jobs pay per year?

As of Jul 29, 2026, the average yearly pay for artificial intelligence machine learning engineer in Philadelphia, PA is $123,068.00, according to ZipRecruiter salary data. Most workers in this role earn between $97,000.00 and $148,100.00 per year, depending on experience, location, and employer.

What is an Artificial Intelligence Machine Learning Engineer?

An Artificial Intelligence (AI) Machine Learning Engineer is a professional who designs, builds, and implements machine learning models and AI systems. They work with large datasets, develop algorithms, and use programming languages like Python or R to enable computers to learn from data and make predictions or decisions. Their work is essential in fields such as natural language processing, computer vision, and robotics. These engineers collaborate with data scientists, software developers, and business stakeholders to deploy AI solutions in real-world applications.

What are some common challenges faced by Artificial Intelligence Machine Learning Engineers when deploying models to production?

One of the main challenges AI/ML engineers encounter is ensuring that models trained in a controlled environment perform reliably in real-world production settings. This often involves handling issues like data drift, scaling models to handle large volumes of requests, and integrating with existing infrastructure. Collaboration with data engineers and software developers is crucial to streamline deployment, monitor model performance, and address any unexpected behavior quickly. Keeping up with evolving tools and best practices is also important for long-term model maintenance and success.

What is the difference between Artificial Intelligence Machine Learning Engineer vs Data Scientist?

AspectArtificial Intelligence Machine Learning EngineerData Scientist
Required CredentialsBachelor's or higher in CS, AI, ML, or related; certifications like TensorFlow, AWSBachelor's or higher in CS, Statistics, or related; certifications in data analysis or visualization
Work EnvironmentDevelops AI/ML models, coding, deploying algorithms in software environmentsAnalyzes data, builds models, interprets data insights for business decisions
Employer & Industry UsageTech companies, AI startups, R&D departmentsFinance, healthcare, marketing, consulting firms

While both roles involve working with data and algorithms, Artificial Intelligence Machine Learning Engineers focus on designing, building, and deploying AI/ML models in software systems. Data Scientists primarily analyze data to extract insights and support decision-making. The roles often overlap but differ in their core focus and daily tasks.

What are the key skills and qualifications needed to thrive as an Artificial Intelligence Machine Learning Engineer, and why are they important?

To thrive as an Artificial Intelligence Machine Learning Engineer, you need strong programming skills (typically in Python or R), a background in mathematics or statistics, and a degree in computer science or a related field. Familiarity with machine learning frameworks (such as TensorFlow, PyTorch, or scikit-learn), cloud platforms, and relevant certifications are highly valuable. Problem-solving ability, creativity, and effective communication are important soft skills that distinguish top performers in this role. These competencies are crucial for designing robust AI solutions, collaborating with cross-functional teams, and driving innovation in rapidly evolving technological environments.
What are popular job titles related to Artificial Intelligence Machine Learning Engineer jobs in Philadelphia, PA? For Artificial Intelligence Machine Learning Engineer jobs in Philadelphia, PA, the most frequently searched job titles are:
What job categories do people searching Artificial Intelligence Machine Learning Engineer jobs in Philadelphia, PA look for? The top searched job categories for Artificial Intelligence Machine Learning Engineer jobs in Philadelphia, PA are:
What cities near Philadelphia, PA are hiring for Artificial Intelligence Machine Learning Engineer jobs? Cities near Philadelphia, PA with the most Artificial Intelligence Machine Learning Engineer job openings:
Artificial Intelligence Machine Learning Engineer jobs near you
Infographic showing various Artificial Intelligence Machine Learning Engineer job openings in Philadelphia, PA as of July 2026, with employment types broken down into 81% Full Time, 16% Part Time, and 3% Contract. Highlights an 89% Physical, 4% Hybrid, and 7% Remote job distribution, with an average salary of $123,068 per year, or $59.2 per hour.
Machine Learning Engineer - Databricks

Machine Learning Engineer - Databricks

Aptino

Malvern, PA • On-site

Other

Posted yesterday

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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