- 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.
- 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
#J-18808-Ljbffr Mercor Inc
