Applied ML Researcher

About the Role

This opportunity is with Mercor, a company that connects highly skilled creative and technical professionals with top AI research organizations. The business 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.

Position Details

You will work as a Machine Learning Engineer Expert on a contract basis in a fully remote setup. Compensation is set at $90 per hour.

Key Responsibilities

• Design and build complete machine learning systems for complex prediction and modeling use cases.
• Study data sets to determine the best modeling approach, testing method, and performance measures.
• Carry out exploratory analysis, create useful features, and prepare data for training.
• Develop, train, fine-tune, and assess models across tabular, text, image, and time-series data.
• Check the technical soundness and quality of machine learning work products and outputs.
• Run structured experiments to find ways to improve model performance.

Must-Have Qualifications

Candidates 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 requires at least 2 years of professional experience in machine learning, applied AI, data science, or a similar field. Strong Python skills and hands-on experience with modern ML libraries and frameworks such as scikit-learn, XGBoost, LightGBM, PyTorch, and TensorFlow are expected. Applicants should have experience delivering end-to-end ML solutions from data preparation through validation and evaluation, along with a solid grasp of model metrics, validation design, and experimentation methods. Familiarity with at least one area such as tabular ML, NLP, computer vision, recommendation systems, ranking, or time-series forecasting is also needed. The ability to work independently on open-ended problems and produce high-quality technical output is essential.

Preferred Background

Additional advantages include a PhD from a leading research university, experience with major technology companies, AI labs, research institutions, or fast-growing startups, and participation in machine learning or data science competitions. It is also beneficial to have experience improving models against performance-based metrics, using advanced methods such as ensembling, hyperparameter tuning, transfer learning, foundation model fine-tuning, or reinforcement learning, and contributing through publications, patents, or open-source work. Prior experience reviewing, mentoring, or evaluating other ML practitioners is a plus.

Application Steps

The application process is expected to take around 20 to 30 minutes. It includes uploading a resume, completing an AI interview based on your resume, and submitting the form.

Resources and Support

Further details about the interview flow and platform can be found in the provided documentation, and support is available through the listed help contact.

Additional Information

The team reviews applications every day. To be considered, candidates should complete both the AI interview and the remaining application steps.