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# Machine Learning Engineer - Expert - AI Trainer

- **Company:** Mercor, Inc.
- **Location:** San Francisco, CA, United States (Remote available)
- **Experience:** Experienced
- **Salary:** $187,200.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Computer Vision, Data Cleansing, Python (Programming Language), Machine Learning, Natural Language Processing, Recommender Systems, Tensorflow, Strategies of Testing, Reinforcement Learning, Feature Engineering, Pytorch, Transfer Learning, Model Validation, Scikit Learn, Information Technology, Xgboost
- **Published:** June 10, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=1eec0384817233fa

## About the Role

Do you have experience in Validation design?, Do you have a Master's degree?, Must-Have

 * Master's degree or PhD in Computer Science, Machine Learning, Statistics, Mathematics, Electrical Engineering, or a related field from a top-tier university.
 * 2+ years of professional experience in machine learning, applied AI, data science, or a closely related field.
 * Strong proficiency in Python and modern machine learning frameworks (e.g., scikit-learn, XGBoost, LightGBM, PyTorch, TensorFlow).
 * Demonstrated experience building end-to-end machine learning solutions, including data preparation, model development, validation, and evaluation.
 * Strong understanding of model evaluation metrics, validation methodologies, and experimental design.
 * Experience with one or more of the following areas: tabular machine learning, natural language processing, computer vision, recommendation systems, ranking systems, time-series forecasting.
 * Ability to work independently on open-ended machine learning problems and deliver high-quality technical outputs.

Preferred

 * PhD from a leading research university.
 * Experience at leading technology companies, AI labs, research institutions, or high-growth startups.
 * Participation in competitive machine learning or data science competitions.
 * Experience optimizing models against performance-based evaluation metrics.
 * Familiarity with advanced techniques such as ensembling, hyperparameter optimization, transfer learning, foundation model fine-tuning, or reinforcement learning.
 * Publications, patents, or significant open-source contributions in machine learning or AI.
 * Experience reviewing, mentoring, or evaluating the work of other machine learning practitioners.

## Description

* Develop end-to-end machine learning solutions for challenging prediction and modeling problems.
 * Analyze datasets and define appropriate modeling approaches, validation strategies, and evaluation metrics.
 * Perform exploratory data analysis, feature engineering, and data preprocessing.
 * Train, tune, and evaluate machine learning models across tabular, text, image, and time-series datasets.
 * Review and validate the technical quality of machine learning projects and deliverables.
 * Identify opportunities to improve model performance through systematic experimentation and iteration.


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