Remote ML Engineer: Ranking, Retrieval & Personalization

Parallel Partners; Oregon; Hybrid; USD 120.000 - 160.000; Vollzeit

Benefits dieser Stelle

Medical insurance

Dental

Vision

Savings Plan

PTO

Zusammenfassung

Parallel Partners, Inc. is seeking a Machine Learning (ML) Engineer for ranking and retrieval tasks in a fully remote role. You will build and improve ML models powering AI-native applications, focusing on classification, retrieval, ranking and personalization, from model development to production deployment.

Must be willing to complete a 60-minute coding assessment. Responsibilities include building ML models, optimizing accuracy, latency and cost, and developing evaluation pipelines.

Qualifikationen

• ML/AI experience required.
• Hands-on experience deploying ML models to production.
• Strong foundations in ML and modern neural architectures.
• Experience in classification, ranking, retrieval or personalization.
• Familiarity with evaluation metrics and experimentation.

Aufgaben

• Build and improve ML models for classification, ranking, retrieval and personalization.
• Develop efficient classifiers and optimize accuracy, latency and cost.
• Improve search relevance and context retrieval using embeddings and ranking techniques.
• Build personalization models learning from user behavior while respecting privacy.
• Develop evaluation pipelines, datasets and metrics to measure model quality.
• Maintain training, inference and deployment pipelines for ML models.

Kenntnisse

Embeddings

Vector search

Ranking models

Production ML systems

Python

PyTorch

JAX

Jobbeschreibung

Parallel Partners, Inc. is seeking a Machine Learning (ML) Engineer for ranking and retrieval tasks in a fully remote role. You will build and improve ML models powering AI-native applications, focusing on classification, retrieval, ranking and personalization, from model development to production deployment.

Must be willing to complete a 60-minute coding assessment. Responsibilities include building ML models, optimizing accuracy, latency and cost, and developing evaluation pipelines.