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Machine Learning Engineer Jobs in Bensalem, PA (NOW HIRING)

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 JobsMachine Learning Engineer Jobs in Bensalem, PA

Machine Learning Engineer information

See Bensalem, PA salary details

$30K

$122.8K

$184.5K

How much do machine learning engineer jobs pay per year?

As of Jul 29, 2026, the average yearly pay for machine learning engineer in Bensalem, PA is $122,755.00, according to ZipRecruiter salary data. Most workers in this role earn between $96,800.00 and $147,800.00 per year, depending on experience, location, and employer.

What engineers make $500,000?

Senior machine learning engineers with extensive experience, advanced skills in deep learning and data science, and often working in high-demand industries or companies can earn $500,000 or more annually. Compensation typically includes base salary, bonuses, and stock options, especially in tech giants or startups with significant funding.

What do machine learning engineers do?

Machine learning engineers develop algorithms and models that enable computers to learn from data and make predictions or decisions. They often work with large datasets, use programming languages like Python or Java, and utilize tools such as TensorFlow or PyTorch to build, test, and deploy machine learning systems in production environments.

What are Machine Learning Engineers?

Machine Learning Engineers are specialized software engineers who design, build, and deploy machine learning models and systems. They work at the intersection of software engineering and data science, transforming data-driven prototypes into scalable, production-ready solutions. Their responsibilities include data preprocessing, model selection, algorithm implementation, and optimizing models for performance and efficiency. Machine Learning Engineers often collaborate with data scientists, software developers, and other stakeholders to integrate AI technologies into products and services.

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

To thrive as a Machine Learning Engineer, you need strong programming skills (particularly in Python), a solid background in mathematics and statistics, and a degree in computer science or a related field. Experience with machine learning frameworks (such as TensorFlow or PyTorch), data processing tools, and cloud platforms is typically required. Problem-solving ability, effective communication, and adaptability are crucial soft skills for collaborating with teams and translating complex models into practical solutions. These competencies ensure the development, deployment, and continual improvement of machine learning systems that drive business value.

Which 5 jobs will survive AI?

Machine Learning Engineers are likely to continue to be in demand as AI advances, as they develop and refine algorithms, models, and systems. Roles that require complex problem-solving, creativity, and domain expertise—such as healthcare professionals, data scientists, software developers, cybersecurity specialists, and AI ethics officers—are also expected to persist due to their reliance on human judgment and specialized knowledge. These jobs often involve skills that are difficult for AI to fully replicate or replace.

What Does a Machine Learning Engineer Do?

A machine learning engineer maintains production systems and often works with other engineers. In this career, you work with software development methodology, use modern software development tools, and use agile practices. You also play a role in software design and architecture, so you may occasionally work with a programmer. An engineer may help to predict how a model should perform or seek out regression issues by using different test types and algorithms. To fulfill your duties and responsibilities, you work on a computer and use an array of skills and programs to carry out these tests.

What engineers make $300,000 a year?

Senior machine learning engineers and data scientists with extensive experience, advanced skills in deep learning, and proficiency with tools like TensorFlow or PyTorch can earn $300,000 or more annually, especially in high-cost-of-living areas or top tech companies. Compensation often includes base salary, bonuses, and stock options, reflecting their expertise and impact on business outcomes.

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

Machine Learning Engineers often encounter challenges such as ensuring model scalability, maintaining data consistency between training and production environments, and monitoring model performance over time. Integrating models into existing software infrastructure may require collaboration with DevOps and software engineering teams to address issues like latency, version control, and resource allocation. Additionally, ongoing model maintenance is crucial to prevent model drift and ensure that predictions remain accurate as new data becomes available.

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

AspectMachine Learning EngineerData Scientist
CredentialsBachelor's or Master's in CS, Data Science, or related; experience with ML frameworksBachelor's or Master's in Statistics, Data Science, or related; strong analytical skills
Work EnvironmentDevelops scalable ML models, deploys algorithms into productionAnalyzes data, builds models, interprets data insights
Industry UsageTech companies, startups, AI-focused firmsFinance, healthcare, marketing, research organizations

While both roles work with data and machine learning, Machine Learning Engineers focus on building and deploying scalable ML models in production environments. Data Scientists primarily analyze data, create models, and generate insights. The roles often overlap but differ in their core responsibilities and focus areas.

More about Machine Learning Engineer jobs
What cities near Bensalem, PA are hiring for Machine Learning Engineer jobs? Cities near Bensalem, PA with the most Machine Learning Engineer job openings:
Machine Learning Engineer jobs near you
Infographic showing various Machine Learning Engineer job openings in Bensalem, PA as of July 2026, with employment types broken down into 93% Full Time, 4% Part Time, and 3% Contract. Highlights an 89% Physical, 4% Hybrid, and 7% Remote job distribution, with an average salary of $122,755 per year, or $59 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.

Apply