**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:
- Natural language processing
- 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