Machine Learning Engineer I

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