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# Machine Learning Engineer IV

- **Company:** Avalara Inc.
- **Location:** Seattle, WA, United States (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Batch Processing, Distributed Systems, Information Retrieval, Python (Programming Language), Machine Learning, Language Modeling, Software Engineering, Large Language Models, Backend, Information Technology, Machine Learning Operations, Document Classification
- **Published:** August 24, 2026
- **Apply:** https://arc.dev/remote-jobs/j/redirect/pelai3bvwe

## About the Role

* B.E. in Computer Science, Engineering, or a related technical field.
 * 6+ years of experience in machine learning engineering, applied ML, NLP, information retrieval, GenAI, or production AI systems.
 * Strong software engineering skills in Python and experience building backend services, APIs, or distributed systems.
 * Experience building, evaluating, and deploying ML models in production environments.
 * Experience with modern ML or AI techniques such as text classification, transformers, embeddings, RAG, LLMs, SLMs, or document AI.
 * Familiarity with model serving, batch and real-time inference, monitoring, observability, and cloud-native deployment.
 * Ability to work across ambiguous business and technical problems and convert them into measurable ML improvements.
 * Strong collaboration and communication skills across engineering, product, and domain stakeholders.

## Description

* Design, build, and improve production ML systems for classification, document understanding, retrieval, and AI-powered automation.
 * Fine-tune and evaluate transformer-based models, small language models, and other ML approaches for practical business use cases.
 * Build and optimize real-time inference services, batch-processing pipelines, APIs, and model-serving workflows.
 * Develop evaluation frameworks to measure model quality, accuracy, latency, reliability, cost, and failure modes.
 * Contribute to GenAI platform capabilities such as RAG, embeddings, prompt systems, document ingestion, and agent workflows.
 * Productionize ML and LLM-powered services with strong reliability, observability, security, and cost controls.
 * Mentor junior engineers and help raise team capability in applied AI and production ML practices.
 * Raise the technical quality and production maturity of AI and ML systems by bringing rigor to how models are evaluated, monitored, and improved.


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