Remote ML Engineer I — Personalization at Scale

GCS Recruitment; Jersey City (NJ); Remote; USD 90,000 - 130,000; Full time

Benefits offered by this job

Remote work option

Job summary

GCS Recruitment is seeking a Machine Learning Engineer I for a remote USA position. This early-career role offers a chance to build production-grade ML solutions, collaborate with data scientists and software engineers, and deliver meaningful user experiences at scale.

You will develop, train, and deploy models for personalization and recommendations, design scalable data pipelines, and ensure robust evaluation, monitoring, and documentation in line with software engineering best practices.

Qualifications

• Bachelor's or Master's in a quantitative field.
• 1–3 years of relevant experience.
• Strong Python programming.
• Experience with ML frameworks (Scikit-learn, TensorFlow, PyTorch).
• Experience with SQL and large datasets.
• Familiarity with Git and software practices.
• Strong analytical and problem-solving skills.

Responsibilities

• Develop, train, and deploy ML models for personalization and recommendations.
• Build scalable data pipelines for feature engineering and training.
• Collaborate with data scientists and engineers to move research to production.
• Improve models using large-scale behavioral data.
• Monitor model performance via experiments and A/B tests.
• Write clean, Python code following best practices.
• Support initiatives to boost customer engagement with ML solutions.

Skills

Python

ML fundamentals

Model evaluation

SQL

Git

Analytical skills

Education

Bachelor's or Master's in quantitative field

Tools

Scikit-learn

TensorFlow

PyTorch

Job description

GCS Recruitment is seeking a Machine Learning Engineer I for a remote USA position. This early-career role offers a chance to build production-grade ML solutions, collaborate with data scientists and software engineers, and deliver meaningful user experiences at scale.

You will develop, train, and deploy models for personalization and recommendations, design scalable data pipelines, and ensure robust evaluation, monitoring, and documentation in line with software engineering best practices.