Job Offer: Remote Machine Learning Engineer

As a Machine Learning Engineer Expert, you will tackle complex machine learning challenges that mirror real-world workflows. This position demands hands-on modeling expertise, the capability to develop high-quality reference solutions, and a deep understanding of modern machine learning techniques across diverse domains and data types.

Key Responsibilities

• Develop end-to-end machine learning 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.

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.

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.