Machine Learning Engineer II- ML Systems, Models and Analysis
Core
Build, evaluate, and operationalize machine learning systems for product matching, classification, similarity search, and automated decisioning.
Role type
Machine Learning Engineer (ML Systems & Analysis)
Builds
Production ML inference pipelines, model-serving workflows, and evaluation frameworks for product matching and search.
Domain
E-commerce / Machine Learning
Deliverable
production ML models
Required skills
Python, SQL, PyTorch, TensorFlow, Hugging Face, scikit-learn, vector search, embeddings, model evaluation, error analysis, LLMs, RAG systems
Preferred skills
NLP, entity resolution, semantic search, recommendation systems, vector databases (Milvus, FAISS, Pinecone), ML Ops tools, dashboarding
Technologies
PyTorch, TensorFlow, Hugging Face, sentence-transformers, Milvus, FAISS, OpenSearch, Pinecone, S3 Vectors
Responsibilities
Build and maintain ML inference pipelines; perform model evaluation and error analysis; analyze model outputs for patterns and failure modes; collaborate on data preparation and labeling strategies; develop analytical scripts and dashboards; debug production ML issues; contribute to reusable ML utilities and experimentation workflows.
Seniority
Mid-level (2–5 years experience)
