Engineer 2 - Machine Learning
Core
Design, develop, and deploy machine learning models across diverse business use cases, including classical ML, deep learning, and Generative AI.
Role type
Machine Learning Engineer
Builds
Production ML models and end-to-end ML pipelines for classification, regression, clustering, and GenAI use cases.
Domain
Media and technology
Deliverable
production ML models
Required skills
Python, classical ML algorithms (Logistic Regression, Decision Trees, Random Forests, SVMs, Gradient Boosting), deep learning frameworks (PyTorch, TensorFlow), feature engineering, statistical analysis, model interpretability (SHAP), data preprocessing, exploratory data analysis (EDA), Transformers/BERT/GPT-style architectures, clean code documentation, model deployment.
Preferred skills
Generative AI (embeddings, summarization, LLM-based solutions), modern architecture experimentation.
Technologies
PyTorch, TensorFlow, SHAP
Responsibilities
Build, train, evaluate, and optimize ML models; engineer high-impact features from raw data; perform data preprocessing and EDA; implement and fine-tune deep learning models; run experiments and communicate results; develop end-to-end ML pipelines; collaborate on model deployment.
Seniority
Mid-level (2-5 years experience)