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