Machine Learning Engineer - ML Systems

Clear Demand; Scottsdale (AZ); On-site; USD 120,000 - 180,000; Full time

Job summary

Clear Demand in Scottsdale, AZ, seeks a machine learning engineering specialist focused on ML systems, model development, evaluation, and inference workflows. You will help build production-ready pipelines for product matching, similarity search, and explainability.

You’ll work with data scientists and engineers to translate model capabilities into scalable workflows, improve performance, and surface actionable insights through dashboards and reports.

Qualifications

• Experience building ML inference pipelines and production-ready models.
• Ability to analyze model outputs, perform error analysis, and measure metrics.
• Strong SQL and Python skills for data extraction and analysis.
• Familiarity with ML ops, model monitoring, and experimentation workflows.

Responsibilities

• Build and maintain ML inference pipelines and model-serving workflows.
• Perform model evaluation, error analysis, and experimentation.
• Analyze outputs to identify patterns, thresholds, and improvements.
• Collaborate with Data Scientists to productionize models.
• Support training data prep, labeling strategy, and benchmarks.
• Work on embeddings, vector search, classification, and matching.
• Develop analytical scripts, notebooks, dashboards, and reports.
• Enhance explainability by surfacing signals and evidence.
• Assist automation where predictions drive product workflows.
• Debug production ML issues like drift and data issues.
• Contribute reusable ML utilities and evaluation frameworks.

Skills

Programming

ML fundamentals

Embeddings

Vector search

Model validation

Python

PyTorch / TensorFlow

SQL

Tools

scikit-learn

PyTorch

TensorFlow

Hugging Face

sentence-transformers

Job description

Role Summary

This role is suited for a candidate with deeper interest and capability in machine learning systems, model development, model evaluation, inference workflows, experimentation, and analytical problem-solving. The candidate will work on model-driven systems that support product matching, classification, similarity search, explainability, automated decisioning, and quality improvement.

The role requires someone who can understand ML model behavior, analyze outputs, improve inference pipelines, measure performance, and collaborate with science and engineering teams to translate model capabilities into production workflows.

Key Responsibilities

• Build, improve, and maintain ML inference pipelines and model-serving workflows.
• Work on model evaluation, error analysis, performance tracking, and experimentation.
• Analyze model outputs to identify patterns, failure modes, precision / recall trade-offs, and improvement opportunities.
• Collaborate with Data Scientists to operationalize models into production-ready systems.
• Support model training data preparation, labeling strategy, validation sample analysis, and benchmark creation.
• Work on ML systems involving embeddings, vector search, retrieval, classification, ranking, and match verdict generation.
• Develop analytical scripts, notebooks, dashboards, and reports to measure model performance and business impact.
• Help improve model explainability by surfacing signals, evidence, decision factors, and confidence indicators.
• Support automation workflows where model predictions are used for decisioning and downstream product workflows.
• Debug production ML issues related to model outputs, drift, thresholds, retrieval quality, and data inconsistencies.
• Contribute to building reusable ML utilities, evaluation frameworks, and experimentation workflows.

Required Skills

• Strong programming skills.
• Good understanding of machine learning fundamentals, including classification, embeddings, similarity search, evaluation metrics, and model validation.
• Experience with ML libraries such as scikit-learn, PyTorch, TensorFlow, Hugging Face, sentence-transformers, or similar frameworks.
• Ability to perform model output analysis, error analysis, and metric-driven evaluation.
• Strong analytical skills with the ability to work with datasets, derive insights, and identify improvement opportunities.
• Good understanding of precision, recall, F1 score, confidence thresholds, false positives, false negatives, and sampling strategies.
• Strong in SQL , Python ML Libraries and data extraction for ML analysis.
• Familiarity with model inference pipelines and production ML workflows.
• Experience with LLMs, prompt-based evaluation, RAG systems, or agentic AI workflows.
• Ability to write clean, modular, maintainable code.
• Good communication skills to explain model behavior, analysis findings, and technical trade-offs.

Good to Have

• Experience with NLP, product matching, entity resolution, semantic search, or recommendation systems.
• Exposure to vector databases or retrieval systems such as Milvus, FAISS, OpenSearch, Pinecone, S3 Vectors, or similar technologies.
• Familiarity with ML Ops tools, model monitoring, experiment tracking, and deployment workflows.
• Experience working with e-commerce catalog data, product attributes, brand, size, pack, and taxonomy-related problems.
• Ability to build dashboards or analysis reports