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# Machine Learning Engineer

- **Company:** The Multi
- **Location:** United States (Remote available)
- **Experience:** Experienced
- **Salary:** $180,000.0 - $200,000.0
- **Contract:** Permanent contract
- **Skills:** Clean Code Principles, Artificial Intelligence, Computer Vision, Big Data, Boolean Algebra, Information Retrieval, Python (Programming Language), Machine Learning, NumPy, Recommender Systems, Tensorflow, Pytorch, Large Language Models, Model Validation, Pandas, Scikit Learn, Information Technology, Xgboost, Machine Learning Operations, Data Pipelines, Software Library
- **Published:** June 3, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=83e7621f8f4b6ce1

## About the Role

Do you have experience in Tooling?, * Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, Statistics, or a similar technical field, or equivalent practical experience.

 * 3+ years of experience building, deploying, or maintaining machine learning systems in production environments.
 * Experience building ML models or systems at scale in areas such as search, recommender systems, personalization, computer vision, predictive modeling, or user-facing ranking systems.
 * Experience working on consumer-facing products where machine learning directly impacts user discovery, engagement, retention, or personalization.
 * Experience working with global-scale systems, high-traffic environments, or large-scale user behavior data.
 * Excellent Python programming skills.
 * Experience with common machine learning libraries, frameworks, and tooling, such as scikit-learn, PyTorch, TensorFlow, XGBoost, pandas, NumPy, or similar tools.
 * Ability to reason through ML system design, including data quality, model evaluation, performance tradeoffs, scalability, reliability, and monitoring.
 * Strong communication skills and experience partnering with Product, Engineering, Data Science, and other cross-functional partners to deliver practical ML solutions for complex product systems.

Bonus points:

 * Experience with search systems, ranking systems, recommendation engines, embeddings, or information retrieval.

## Description

We are looking for a Machine Learning Engineer to join our Artificial Intelligence and Machine Learning team. In this role, you will help build, maintain, and improve production grade machine learning systems that support search, recommendations, personalization, computer vision, predictive modeling, and AI-driven experimentation across a global-scale consumer platform.

This is a hands-on engineering role for someone who has experience building ML models and systems at scale, understands how to evaluate model performance in production contexts, and is excited to apply machine learning to practical product and business problems.

You'll partner closely with Data Scientists, Product, Engineering, and other cross-functional partners to translate product and business needs into scalable ML solutions that improve discovery, engagement, retention, and user experience.

You Will:

 * Build, maintain, and improve production machine learning systems that support search, recommendations, personalization, computer vision, and predictive modeling.
 * Contribute to search and discovery improvements, including ranking, filtering, relevance, exact match, boolean logic, and LLM-powered enhancements.
 * Develop and integrate machine learning models that improve recommendation quality, search accuracy, behavioral analytics, and personalized user experiences.
 * Write clean, reliable, and maintainable code for ML pipelines, model development, experimentation, and production workflows.
 * Work with large-scale datasets to train, evaluate, monitor, and improve ML systems.
 * Collaborate with Data Science, Product, Engineering, and other cross-functional partners to understand requirements, evaluate tradeoffs, and deliver ML solutions that create measurable product and business impact.
 * Participate in technical design discussions for ML systems, including model architecture, data pipelines, evaluation methods, deployment approaches, monitoring, and scalability.


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