Machine Learning Engineer Expert

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.

Develop end-to-end machine le.arning solutions for challenging prediction and modeling problems

1. Role Overview

2. What You’ll Do

3. Required Qualifications

4. Preferred Qualifications

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. 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 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 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 We consider all qualified applicants without regard to legally protected characteristics and provide reasonable accommodations upon request.

• 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
• 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

• 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
• Tabular machine learning
• Natural language processing
• Computer vision
• Recommendation systems
• Ranking systems
• Time-series forecasting
• 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