Applied Machine Learning (ML) Engineer, Machine Learning (ML) and ArtificialIntelligence (AI) Requi

Parallel Partners; 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

Job DescriptionApplied Machine Learning (ML) Engineer, Machine Learning (ML) and Artificial Intelligence (AI) Required, Work From HomeThe 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.

Applied Machine Learning (ML) Engineer Responsibilities

• 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.

Applied Machine Learning (ML) Engineer Outcomes

• 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.

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Additional Information

All your information will be kept confidential according to EEO guidelines. Compensation: USD 120000 - USD 160000 - yearly