ML Systems Engineer: Inference Pipelines & Vector Search

Clear Demand; United States; Remote; USD 120,000 - 190,000; Full time

Job summary

Clear Demand is seeking a technically skilled ML engineer to advance model-driven systems for product matching, classification, and similarity search. You will design and maintain production-ready inference pipelines, evaluate model performance, and collaborate with data scientists to translate research into scalable solutions.

The role emphasizes experimentation, benchmarking, and improving explainability, with opportunities to impact real-time decisioning and downstream product workflows.

Qualifications

• Proficient in ML fundamentals including classification, embeddings, similarity search, and evaluation metrics.
• Experience with ML libraries such as scikit-learn, PyTorch, TensorFlow, Hugging Face, or similar frameworks.
• Strong analytical skills with ability to derive insights from data and identify improvement opportunities.
• Experience with production ML workflows and inference pipelines.
• Good communication to explain model behavior and trade-offs.

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.

Skills

Programming

ML fundamentals

Data analysis

Model evaluation

Explainability

Communication

Python

SQL

Team collaboration

Tools

scikit-learn

PyTorch

TensorFlow

Hugging Face

sentence-transformers

Milvus

FAISS

Job description

Clear Demand is seeking a technically skilled ML engineer to advance model-driven systems for product matching, classification, and similarity search. You will design and maintain production-ready inference pipelines, evaluate model performance, and collaborate with data scientists to translate research into scalable solutions.

The role emphasizes experimentation, benchmarking, and improving explainability, with opportunities to impact real-time decisioning and downstream product workflows.