Applied ML Researcher

About the role

Mercor builds a bridge between top-tier creative and technical professionals and leading AI research organizations. The company is based in San Francisco and is backed by investors such as Benchmark, General Catalyst, Peter Thiel, Adam D'Angelo, Larry Summers, and Jack Dorsey.

This opening is for a Machine Learning Engineer Expert working remotely on contract basis, with compensation set at $90 per hour.

Core responsibilities

• Create complete machine learning solutions for complex prediction and modeling challenges.
• Examine datasets and choose suitable modeling methods, validation plans, and success metrics.
• Carry out exploratory analysis, build features, and prepare data for modeling.
• Build, train, tune, and assess models across tabular, text, image, and time-series use cases.
• Evaluate the technical soundness and quality of machine learning work products and project outputs.
• Use structured experimentation and repeated iteration to raise model performance.

Qualifications

Applicants should hold a Master's degree or PhD in Computer Science, Machine Learning, Statistics, Mathematics, Electrical Engineering, or a closely related discipline from a top-tier university. The role also calls for at least 2 years of professional experience in machine learning, applied AI, data science, or a similar area.

Strong Python skills and hands-on experience with modern machine learning libraries and frameworks such as scikit-learn, XGBoost, LightGBM, PyTorch, and TensorFlow are required. Candidates must have proven experience delivering complete ML pipelines, including data preparation, model building, validation, and evaluation.

Knowledge of evaluation metrics, validation strategies, and experimental design is essential. Experience in one or more of the following areas is needed: tabular ML, natural language processing, computer vision, recommendation systems, ranking systems, or time-series forecasting. The ability to work independently on ambiguous ML problems and produce high-quality technical output is also expected.

Preferred background

• A PhD from a leading research university.
• Experience with major technology companies, AI research labs, research institutions, or fast-growing startups.
• Participation in machine learning or data science competitions.
• Experience improving models against performance-based evaluation criteria.
• Exposure to advanced methods such as ensembling, hyperparameter tuning, transfer learning, foundation model fine-tuning, or reinforcement learning.
• Publications, patents, or meaningful open-source contributions in ML or AI.
• Experience reviewing, guiding, or assessing the work of other machine learning professionals.

Application process

The application takes about 20 to 30 minutes and includes uploading a resume, completing an AI interview based on that resume, and submitting the form.

Resources and support

For more information about the interview flow and platform details, a documentation resource is available. Support is available through the provided help email.

Additional information

The hiring team reviews applications every day. Candidates should finish both the AI interview and the remaining application steps to be considered.