Machine Learning Engineer I

NextGen | GTA: A Kelly Telecom Company; West Chester (Chester County); On-site; USD 85,000 - 125,000; Full time

Job summary

NextGen | GTA: A Kelly Telecom Company is seeking a Machine Learning Engineer I to turn algorithms into working products. You will build data pipelines, train models, and support deployments across the full development cycle using Python, Java, Scala, and cloud tools.

Early-career ML engineers will contribute to proof-of-concept projects, improve reliability of models in production, and document findings to support ongoing evaluation and scaling of ML solutions.

Qualifications

• 1-3 years of related experience after completing a bachelor's degree.
• Experience with machine learning, deep learning, data mining, or statistical analysis.
• Strong programming and software development skills.
• Familiarity with Python, Java, or Scala.
• Experience building or supporting data pipelines, including data ingestion, validation, cleaning, and monitoring.
• Bachelor's degree in Computer Science, Computer Engineering, Mathematics, or a related technical discipline—or equivalent industry experience

Responsibilities

• Implement, refine, and validate machine learning algorithms for products and applications.
• Build and maintain data pipelines for ingestion, validation, cleaning, and monitoring.
• Train machine learning models and evaluate their accuracy and performance.
• Deploy validated models into production and support ongoing monitoring.
• Contribute to proof-of-concept projects, case studies, testing, and technical evaluations.
• Research and prepare documentation, requirements, reports, presentations, and recommendations

Skills

Python

Java

Scala

Machine learning

Data pipelines

Education

Bachelor's degree in Computer Science or related field

Tools

Kafka

Spark

Docker

Cloud technologies

Job description

Bring your programming and data skills to a Machine Learning Engineer I role focused on turning algorithms into working products and applications. You'll help build data pipelines, train and validate machine learning models, support production deployments, and evaluate solutions across the full development cycle. This is a hands-on opportunity to work with Python, Java, Scala, cloud technologies, and modern data-processing tools.

If you're early in your machine learning career and want to see your work move beyond experimentation, this role offers a strong mix of model development, data engineering, testing, and technical research. You'll contribute to proof-of-concept projects, improve the reliability of machine learning solutions, and build practical experience documenting and supporting models in production.

Required Skills & Experience

• 1-3 years of related experience after completing a bachelor's degree
• Experience with machine learning, deep learning, data mining, or statistical analysis
• Strong programming and software development skills
• Familiarity with Python, Java, or Scala
• Experience building or supporting data pipelines, including data ingestion, validation, cleaning, and monitoring
• Bachelor's degree in Computer Science, Computer Engineering, Mathematics, or a related technical discipline—or equivalent industry experience

Desired Skills & Experience

• Experience training, validating, deploying, and monitoring machine learning models
• Familiarity with Kafka, Spark, Docker, or similar data and cloud technologies
• Experience contributing to proof-of-concept solutions
• Exposure to model accuracy testing, performance evaluation, or production monitoring
• Experience writing technical documentation, evaluation plans, test reports, or presentations

What You Will Be Doing

• Implement, refine, and validate machine learning algorithms for products and applications
• Build and maintain data pipelines for ingestion, validation, cleaning, and monitoring
• Train machine learning models and evaluate their accuracy and performance
• Deploy validated models into production and support ongoing monitoring
• Contribute to proof-of-concept projects, case studies, testing, and technical evaluations
• Research and prepare documentation, requirements, reports, presentations, and recommendations

Tech Breakdown

• 30% Machine Learning Model Development and Validation
• 25% Data Pipeline Development and Monitoring
• 20% Programming and Software Development
• 15% Model Deployment and Production Support
• 10% Testing, Documentation, and Technical Research

Daily Responsibilities

• Develop and refine machine learning algorithms
• Prepare, validate, and monitor data used for model training
• Train models and review accuracy, performance, and test results
• Support model deployment and production monitoring
• Test proof-of-concept solutions and document findings
• Create technical requirements, evaluation plans, reports, and supporting documentation

Posted By: Nicole Screnci