Remote ML Engineer - New Grad, Search & Recommendations

Jobright.ai; United States; Remote; USD 90,000 - 120,000; Full time

Job summary

Jobright.ai seeks a Machine Learning Engineer (New Grad) to contribute to model development, data pipelines, evaluation systems, and ML features across search and recommendations.

You will work with AI engineers, software engineers, data scientists, and product teams to build end-to-end ML solutions in a remote-friendly environment. Recent graduates in ML/CS/stats are encouraged to apply, with emphasis on understanding supervised learning and model evaluation.

Qualifications

• Recent graduate or graduating soon in Machine Learning, Computer Science, Statistics, Mathematics, or a related field.
• Understanding of supervised learning, model evaluation, and statistics.
• Familiarity with NumPy, pandas, and scikit-learn or similar tools.
• Ability to work with data and communicate technical findings clearly.
• Ability to work remotely.

Responsibilities

• Develop and evaluate machine-learning models for search and recommendations
• Prepare and analyze datasets for model training and evaluation
• Build data and model evaluation pipelines
• Experiment with ranking, classification, NLP, and personalization techniques
• Monitor model quality and investigate performance issues
• Collaborate with AI engineers, software engineers, data scientists, and product teams

Skills

Supervised learning

Statistics

Communication of findings

Remote work ability

Education

Recent graduate in ML/CS/stats

Tools

NumPy

pandas

scikit-learn

PyTorch

Job description

Jobright.ai seeks a Machine Learning Engineer (New Grad) to contribute to model development, data pipelines, evaluation systems, and ML features across search and recommendations.

You will work with AI engineers, software engineers, data scientists, and product teams to build end-to-end ML solutions in a remote-friendly environment. Recent graduates in ML/CS/stats are encouraged to apply, with emphasis on understanding supervised learning and model evaluation.