Machine Learning Engineer

Bright Vision Technologies • Frisco, TX

5d • $115,000 - $165,000 • Remote

About The Position

Bright Vision Technologies is seeking a Machine Learning Engineer to develop, train, evaluate, and operationalize predictive and classification models for production applications. The engineer will work across data preparation, feature engineering, model experimentation, validation, and deployment, partnering with data scientists and software teams to deliver measurable model performance and maintainable services. This is a remote, full-time, direct W-2 position.

Requirements

• Typically 5+ years in machine learning engineering, applied data science, or related production model development.
• Strong Python skills and practical experience with libraries such as scikit-learn, pandas, NumPy, or equivalent tools.
• Understanding of supervised and unsupervised learning, model evaluation, feature engineering, and statistical fundamentals.
• Experience with at least one deep-learning framework such as PyTorch or TensorFlow, where relevant to the project.
• Experience building reproducible data and model pipelines and managing code through Git.
• Ability to deploy or integrate model inference into APIs, batch workflows, or application services.
• Strong analytical reasoning, experimentation discipline, and communication of model trade-offs.
• Experience working in iterative software delivery teams, participating in planning, defect triage, and release validation as appropriate to the assignment.

Nice To Haves

• Experience with distributed training, feature stores, experiment tracking, or model registries.
• Exposure to cloud machine learning services and containerized deployment.
• Familiarity with model explainability, bias evaluation, and production monitoring practices.
• Relevant machine learning or cloud platform certification.

Responsibilities

• Prepare datasets, engineer features, and build reproducible training and validation pipelines.
• Develop, tune, and evaluate machine learning models for classification, regression, ranking, or forecasting use cases.
• Implement model-serving components and integrate inference workflows with application and data platforms.
• Track model performance, investigate drift or quality degradation, and recommend retraining or improvement actions.
• Document experiments, model assumptions, evaluation results, and implementation decisions for engineering stakeholders.

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed