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

Parallel Partners, Inc.; San Francisco (CA); Remote; USD 120,000 - 160,000; Full time

Benefits offered by this job

Medical insurance

Dental and Vision

Job summary

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

The role requires practical ML/AI experience, strong Python and PyTorch/JAX skills, and a focus on scalable, low-latency pipelines. This position is 100% remote within the United States.

Qualifications

• ML experience is required.
• AI experience is required.
• Strong foundations in ML and modern neural architectures.
• Hands-on experience building and deploying ML models in production.
• Experience in classification, ranking, retrieval, recommendation systems or personalization.
• Understanding of model evaluation, experimentation and performance metrics.
• Comfortable writing production-quality code and working with real-world datasets.
• Able to balance model quality, latency, cost and reliability.
• Strong ownership, problem-solving skills.

Responsibilities

• Build and improve ML models for classification, ranking, retrieval and personalization.
• Develop efficient classifiers and optimize model accuracy, 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 data supports.
• Build and maintain ML training, inference and deployment pipelines.
• Debug production issues, monitor model performance and ship improvements based on real-world feedback.
• Model improvements are driven by real-world feedback and measurable results.
• Collaborates effectively with engineering and product teams to deliver reliable ML-powered features.

Skills

ML experience

AI experience

Neural architectures

Production-ready code

Datasets handling

Tools

Python

PyTorch

JAX

Vector search

Job description

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

• Full-time
• Compensation: USD 120,000 - USD 160,000 - yearly

Applied Machine Learning (ML) Engineer will build and improve Machine Learning (ML) systems that power our AI-native applications. You will work on classification, retrieval, ranking, personalization and evaluation, taking ownership from model development to production deployment. This role is for engineers who enjoy solving practical Machine Learning (ML) problems, improving model quality and shipping reliable systems used by real users. Machine Learning (ML) and Artificial Intelligence (AI) experience are required. This position is 100% Remote.

MUST BE WILLING TO TAKE A 60 MINUTE CODING ASSESSMENT.

• Build and improve Machine Learning (ML) 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 (ML) training, inference and deployment pipelines.
• Debug production issues, monitor model performance and ship improvements based on real-world feedback.
• Machine Learning (ML) models in production meet expected accuracy, relevance, latency and reliability targets.
• Classification and ranking improvements deliver measurable gains in model quality.
• Evaluation pipelines identify regressions and support reliable model releases.
• Training, inference and data pipelines are robust, reproducible and maintainable.
• Model improvements are driven by real-world feedback and measurable results.
• Collaborates effectively with engineering and product teams to deliver reliable ML-powered features.

Applied Machine Learning (ML) Engineer Qualifications:

• Machine Learning (ML) experience is required.
• Artificial Intelligence (AI) experience is required.
• Strong foundations in Machine Learning (ML) and modern neural architectures.
• Hands-on experience building and deploying Machine Learning (ML) models in production.
• Experience in classification, ranking, retrieval, recommendation systems or personalization.
• Understanding of model evaluation, experimentation and performance metrics.
• Comfortable writing production-quality code and working with real-world datasets.
• Able to balance model quality, latency, cost and reliability.
• Strong ownership, problem-solving skills
• Tech Stack: Embeddings, vector search and ranking models, Production Machine Learning (ML) systems and inference pipelines, Python, and PyTorch/JAX.

Benefits include medical insurance, Dental, Vision, Savings Plan Options, PTO, etc.

All your information will be kept confidential according to EEO guidelines.

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