Applied ML Researcher - Fully Remote | Upto $90/hr

<h3>About the job</h3><p><strong>Mercor</strong> connects elite creative and technical talent with leading AI research labs. Headquartered in San Francisco, our investors include <strong>Benchmark</strong>, <strong>General Catalyst</strong>, <strong>Peter Thiel</strong>, <strong>Adam D'Angelo</strong>, <strong>Larry Summers</strong>, and <strong>Jack Dorsey</strong>.</p><p><strong>Position:</strong> Machine Learning Engineer Expert<br><strong>Type:</strong><strong>Contract</strong><br><strong>Compensation:</strong><strong>$90/hour</strong><br><strong>Location:</strong><strong>Remote</strong></p><h3>Role Responsibilities</h3><ul><li>Develop end-to-end <strong>machine learning</strong> solutions for challenging prediction and modeling problems.</li><li>Analyze datasets and define appropriate modeling approaches, validation strategies, and evaluation metrics.</li><li>Perform exploratory data analysis, feature engineering, and data preprocessing.</li><li>Train, tune, and evaluate <strong>machine learning models</strong> across tabular, text, image, and time-series datasets.</li><li>Review and validate the technical quality of <strong>machine learning</strong> projects and deliverables.</li><li>Identify opportunities to improve model performance through systematic experimentation and iteration.</li></ul><h3>Qualifications<p></p><p><strong>Must-Have</strong></p></h3><ul><li><strong><strong>Master's degree</strong> or <strong>PhD</strong> in <strong>Computer Science</strong>, <strong>Machine Learning</strong>, <strong>Statistics</strong>, <strong>Mathematics</strong>, <strong>Electrical Engineering</strong>, or a related field from a top-tier university.</strong></li><li><strong><strong>2+ years</strong> of professional experience in <strong>machine learning</strong>, applied <strong>AI</strong>, data science, or a closely related field.</strong></li><li><strong>Strong proficiency in <strong>Python</strong> and modern <strong>machine learning frameworks</strong> (e.g., <strong>scikit-learn</strong>, <strong>XGBoost</strong>, <strong>LightGBM</strong>, <strong>PyTorch</strong>, <strong>TensorFlow</strong>).</strong></li><li><strong>Demonstrated experience building end-to-end <strong>machine learning</strong> solutions, including data preparation, model development, validation, and evaluation.</strong></li><li><strong>Strong understanding of model evaluation metrics, validation methodologies, and experimental design.</strong></li><li><strong>Experience with one or more of the following areas: tabular <strong>machine learning</strong>, natural language processing, computer vision, recommendation systems, ranking systems, time-series forecasting.</strong></li><li><strong>Ability to work independently on open-ended <strong>machine learning</strong> problems and deliver high-quality technical outputs.</strong></li></ul><h3><strong>Preferred</strong></h3><ul><li><strong><strong>PhD</strong> from a leading research university.</strong></li><li><strong>Experience at leading technology companies, <strong>AI</strong> labs, research institutions, or high-growth startups.</strong></li><li><strong>Participation in competitive <strong>machine learning</strong> or data science competitions.</strong></li><li><strong>Experience optimizing models against performance-based evaluation metrics.</strong></li><li><strong>Familiarity with advanced techniques such as ensembling, hyperparameter optimization, transfer learning, foundation model fine-tuning, or reinforcement learning.</strong></li><li><strong>Publications, patents, or significant open-source contributions in <strong>machine learning</strong> or <strong>AI</strong>.</strong></li><li><strong>Experience reviewing, mentoring, or evaluating the work of other <strong>machine learning</strong> practitioners.</strong></li></ul><h3><strong>Application Process (Takes 20–30 mins to complete)</strong></h3><ul><li><strong>Upload resume</strong></li><li><strong>AI interview based on your resume</strong></li><li><strong>Submit form</strong></li></ul><h3><strong>Resources &amp; Support</strong></h3><ul><li><strong>For details about the interview process and platform information, please check: https://talent.docs.<a href="https://himalayas.app/companies/mercor">mercor</a>.com/welcome</strong></li><li><strong>For any help or support, reach out to: support@<a href="https://himalayas.app/companies/mercor">mercor</a>.com</strong></li></ul><p><strong><em>PS: Our team reviews applications daily. Please complete your AI interview and application steps to be considered for this opportunity.</em></strong></p><p>Originally posted on <a href="https://himalayas.app">Himalayas</a></p>