Machine Learning Engineer Expert

About this role

# **1\. Role Overview** We’re hiring experienced Machine Learning Engineers and Applied ML Researchers to design, solve, and evaluate complex machine learning challenges that reflect real-world ML workflows. This role requires strong hands-on modeling expertise, the ability to develop high-quality reference solutions, and deep familiarity with modern machine learning techniques across a variety of domains and data modalities. # **2\. What You’ll Do** - Develop end-to-end machine le.arning 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 - Develop strong reference solutions using industry-standard machine learning techniques and best practices - Review and validate the technical quality of machine learning projects and deliverables - Document methodologies, assumptions, and evaluation results in a clear and reproducible manner - Identify opportunities to improve model performance through systematic experimentation and iteration # **3\. Required Qualifications** - 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 hands-on experience developing, training, evaluating, and optimizing machine learning models in a professional or research setting. - 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 # **4\. Preferred Qualifications** - 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

Skills & domains

• ai-training
• rlhf
• sme
• annotation
• Data Analysis