Applied Machine Learning (ML) Engineer, Machine Learning (ML) and Artificial Intelligence (AI) Required, Work From Home

Parallel Partners•San Francisco, CA

6d•$120,000 - $160,000•Remote

About The Position

The Applied Machine Learning Engineer will build and improve machine learning systems that power AI-native applications. The role covers classification, retrieval, ranking, personalization, and evaluation, with ownership extending from model development through production deployment. The engineer will solve practical machine learning problems, improve model quality, and ship reliable systems used by real users. Machine learning and artificial intelligence experience are required. This position is 100% remote. Applicants must be willing to take a 60-minute coding assessment.

Requirements

Machine learning experience is required.
Artificial intelligence experience is required.
Strong foundations in machine learning and modern neural architectures.
Hands-on experience building and deploying machine learning models in production.
Experience in classification, ranking, retrieval, recommendation systems, or personalization.
Understanding of model evaluation, experimentation, and performance metrics.
Ability to write production-quality code and work with real-world datasets.
Ability to balance model quality, latency, cost, and reliability.
Strong ownership and problem-solving skills.
Willingness to take a 60-minute coding assessment.

Responsibilities

Build and improve machine learning models for classification, ranking, retrieval, and personalization.
Develop efficient classifiers and optimize model accuracy, precision, latency, and cost.
Improve search relevance and context retrieval using embeddings, hybrid search, and reranking.
Build personalization models that learn from user behavior while respecting privacy.
Develop evaluation pipelines, labeled datasets, and metrics to measure model quality.
Fine-tune and adapt existing models when supported by sufficient data.
Build and maintain machine learning training, inference, and deployment pipelines.
Debug production issues, monitor model performance, and ship improvements based on real-world feedback.