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
#J-18808-Ljbffr