Remote Applied ML Engineer — Production-Ready Models

Parallel Partners, Inc.; Oregon; Hybrid; USD 120,000 - 160,000; Full time

Benefits offered by this job

Medical insurance

Dental insurance

Vision insurance

Savings plan options

PTO

Job summary

Parallel Partners, Inc. is seeking an Applied Machine Learning Engineer to build and improve ML models powering AI-native applications. You will handle classification, ranking, retrieval and personalization from model development to production deployment.

The role is 100% remote, with a willingness to complete a 60-minute coding assessment. Technical focus includes embeddings, vector search, and modern neural architectures using Python and PyTorch/JAX.

Qualifications

• ML/AI experience required.
• Hands-on experience deploying ML models in production.
• Experience with classification, ranking, retrieval or personalization.
• Strong ownership and problem-solving skills.
• Comfort writing production-quality code with real-world datasets.

Responsibilities

• Build and improve ML models for classification, ranking, retrieval and personalization.
• Develop efficient classifiers optimizing accuracy, latency and cost.
• Improve search relevance using embeddings, hybrid search and reranking.
• Develop evaluation pipelines, datasets and metrics to measure model quality.
• Fine-tune and adapt existing models with sufficient data.
• Build and maintain training, inference and deployment pipelines.
• Debug production issues and monitor model performance.

Skills

ML model development

Production ML

Ownership

Problem solving

Code quality

Tools

Python

PyTorch

JAX

Embeddings

Job description

Parallel Partners, Inc. is seeking an Applied Machine Learning Engineer to build and improve ML models powering AI-native applications. You will handle classification, ranking, retrieval and personalization from model development to production deployment.

The role is 100% remote, with a willingness to complete a 60-minute coding assessment. Technical focus includes embeddings, vector search, and modern neural architectures using Python and PyTorch/JAX.