About the job
Mercor connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include Benchmark , General Catalyst , Peter Thiel , Adam D'Angelo , Larry Summers , and Jack Dorsey .
Position: Machine Learning Engineer Expert Type: Contract Compensation: $90/hour Location: Remote
Role Responsibilities
- 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.
Qualifications
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.
Application Process (Takes 20–30 mins to complete)