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