Machine Learning (ML) Engineer, Ranking & Retrieval, 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

Vision

Savings plan

PTO

Job summary

Parallel Partners, Inc. is seeking a Machine Learning (ML) Engineer to build and improve ML systems powering AI-driven applications.

You will work on classification, retrieval, ranking, personalization and evaluation, taking ownership from model development to production deployment in a fully remote environment. The role requires ML and AI experience, strong foundations in modern neural architectures, and hands-on deployment of models.

Qualifications

• Machine Learning experience is required.
• Artificial Intelligence 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 and problem-solving skills.
• Tech Stack: Embeddings, vector search and ranking models, Python and PyTorch/JAX.

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 allows.
• Build and maintain ML training, inference and deployment pipelines.
• Debug production issues, monitor model performance and ship improvements.
• Ensure ML models meet accuracy, relevance, latency and reliability targets.
• Collaborate with engineering and product teams to deliver ML-powered features.

Skills

ML experience

AI experience

Neural architectures

Production code

Classification

Ranking

Retrieval

Personalization

Model evaluation

Datasets

Ownership

Python

PyTorch/JAX

Tools

Embeddings

Vector search

Ranking models

Production ML systems

Inference pipelines

Python

PyTorch/JAX

Job description

Machine Learning (ML) Engineer, Ranking & Retrieval, Machine Learning (ML) and Artificial Intelligence (AI) Required, Work From Home

• Full-time


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

Machine Learning (ML) Engineer, Ranking & Retrieval, Machine Learning (ML) and Artificial Intelligence (AI) Required, Work From Home

The 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.

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.

Keywords: San Francisco CA Jobs, AI, Artificial Intelligence, Datasets, Embedding, JAX, Machine Learning (ML) Classification, Machine Learning (ML) Engineer, Machine Learning (ML) Inference Pipelines, Machine Learning (ML) Models, Machine Learning (ML) Performance Metrics, Machine Learning (ML) Personalization, Machine Learning (ML) Ranking, Machine Learning (ML) Recommendation Systems, Machine Learning (ML) Retrieval, ML, Machine Learning, Modern Neural Architectures, Production Machine Learning (ML) Systems, Python, PyTorch, Vector Search and Ranking Models, Work From Home, Remote, California Recruiters, IT Jobs, California Recruiting

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