[Remote] Machine Learning Engineer Expert

Note: The job is a remote job and is open to candidates in USA. Crossing Hurdles is seeking a Machine Learning Engineer Expert to build, evaluate, and improve real-world machine learning solutions on a flexible remote contract. The role involves developing end-to-end machine learning solutions, analyzing datasets, and improving model performance through systematic experimentation.

Responsibilities

• Develop end-to-end machine learning solutions for prediction and modeling problems
• Analyze datasets and choose appropriate modeling approaches, validation strategies, and metrics
• Perform exploratory data analysis, preprocessing, and feature engineering
• Train, tune, and evaluate models across tabular, text, image, and time-series datasets
• Build strong reference solutions using industry-standard ML techniques and best practices
• Review technical quality, document assumptions, and communicate results clearly
• Improve model performance through systematic experimentation and iteration

Skills

• 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 ML models in a professional or research setting
• Strong Python skills and experience with tools such as scikit-learn, XGBoost, LightGBM, PyTorch, or TensorFlow
• Experience building complete ML workflows, from data preparation through validation and evaluation
• Strong understanding of evaluation metrics, validation methodologies, and experimental design
• Ability to work independently on open-ended technical problems and deliver high-quality outputs
• PhD from a leading research university
• Experience at technology companies, AI labs, research institutions, or high-growth startups
• Competitive ML/data science experience
• Experience with ensembling, hyperparameter optimization, transfer learning, foundation model fine-tuning, or reinforcement learning
• Publications, patents, open-source contributions, or experience mentoring/reviewing ML work

Benefits

• Flexible schedule, with projects that may be extended, shortened, or concluded based on needs and performance.
• Weekly payments through Stripe or Wise based on services rendered.

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