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