Machine Learning (ML) Engineer, Ranking & Retrieval, Machine Learning (ML) and Artificial Intelligen

Job Description

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.
Machine Learning (ML) Engineer Responsibilities:
- Build and improve Machine Learning (ML) models for classification, ranking, retrieval and personalization.
- 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) 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.
- 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.
- 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.
- 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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