[Hiring] Machine Learning Engineer @Parallel Partners

Role Description

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.

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.

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.

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 and problem-solving skills.
• Tech Stack: Embeddings, vector search and ranking models, Production Machine Learning (ML) systems and inference pipelines, Python, and PyTorch/JAX.

Benefits

• Medical insurance
• Dental
• Vision
• Savings Plan Options
• PTO

Compensation

USD 120,000 - USD 160,000 yearly