Machine Learning Engineer

GCS Recruitment; Seattle (WA); On-site; USD 110,000 - 140,000; Full time

Job summary

GCS Recruitment is seeking a Machine Learning Engineer I in Seattle to develop, deploy, and optimize ML solutions for ad monetization and personalization. You will build scalable ML pipelines, improve recommendation and targeting models, and deliver production-ready AI solutions that boost user engagement and business outcomes.

The role involves collaborating with ML scientists, data engineers, and software engineers to bring research into production, maintain data pipelines, and monitor model

Qualifications

• Bachelor's or Master's degree in Computer Science, ML, AI, Statistics, Mathematics, or related field.
• 1–3 years of experience in machine learning, internships, research or professional experience.
• Strong programming skills in Python.
• Solid understanding of ML concepts including supervised learning, feature engineering, model evaluation, classification and regression.
• Experience with ML frameworks such as Scikit-learn, TensorFlow, or PyTorch.
• Experience working with SQL and large datasets.
• Familiarity with Git and software development best practices.

Responsibilities

• Develop, train, and deploy ML models for personalization, recommendation, and customer experience applications.
• Build and maintain scalable data pipelines for feature engineering, model training, validation, and inference.
• Collaborate with ML scientists, data engineers, and software engineers to transition research models into production.
• Enhance recommendation and ranking models using customer behavioral and transactional data.
• Monitor, evaluate, and improve model performance through experimentation and A/B testing.

Skills

Python

Feature engineering

Model evaluation

Supervised learning

Classification/Regression

Big datasets

Education

Bachelor's or Master's degree in CS/ML/AI/Statistics/Math

Tools

Scikit-learn

TensorFlow

PyTorch

SQL

Git

Job description

Machine Learning Engineer

Position Overview

We are looking for a Machine Learning Engineer I with strong Python programming skills to develop, deploy, and optimize machine learning solutions for ad monetization and personalization. You'll collaborate with cross-functional teams to build scalable ML pipelines, enhance recommendation and targeting models, and deliver production-ready AI solutions that improve user engagement and business outcomes.

Key Responsibilities

• Develop, train, and deploy machine learning models for personalization, recommendation, and customer experience applications.
• Build and maintain scalable data pipelines for feature engineering, model training, validation, and inference.
• Collaborate with machine learning scientists, data engineers, and software engineers to transition research models into production.
• Enhance recommendation and ranking models using customer behavioural and transactional data.
• Monitor, evaluate, and improve model performance through experimentation, A/B testing, and data-driven analysis.
• Write clean, efficient, and maintainable Python code following software engineering best practices.
• Optimize machine learning workflows to improve customer engagement and support business objectives.
• Participate in model deployment, monitoring, troubleshooting, and continuous improvement initiatives.
• Document machine learning processes, model performance, and technical solutions to support collaboration and knowledge sharing.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, Statistics, Mathematics, or a related field.
• 1-3 years of experience in machine learning, including internships, research, or professional experience.
• Strong programming skills in Python.
• Solid understanding of machine learning concepts, including supervised learning, feature engineering, model evaluation, classification, and regression.
• Experience with machine learning frameworks such as Scikit-learn, TensorFlow, or PyTorch.
• Experience working with SQL and large datasets.
• Familiarity with Git and software development best practices.

What You'll Learn

• Build and deploy production-ready machine learning models at scale.
• Gain hands-on experience with recommendation systems, personalization, and ranking algorithms.
• Learn best practices for feature engineering, model monitoring, and performance optimization.
• Work with large-scale data processing and modern machine learning pipelines.
• Develop expertise in experimentation frameworks, A/B testing, and data-driven decision-making.
• Collaborate with experienced machine learning engineers, software engineers, and data scientists on real-world AI solutions.
• Strengthen your understanding of MLOps concepts, including model deployment, versioning, and lifecycle management.
• Improve your software engineering skills by contributing to scalable, maintainable, and production-quality codebases.

GCS is acting as an Employment Business in relation to this vacancy.