Machine Learning Engineer V

About the Role

Avalara is accelerating its AI-first transformation by building intelligent systems that automate complex compliance workflows and enable product teams to deliver AI-powered experiences at scale. We are investing in senior ML leadership to build the next generation of production AI capabilities.

What You'll Do

• Lead technically complex AI initiatives across classification, document intelligence, GenAI platforms, retrieval, agentic workflows, and production ML systems.
• Operate end-to-end across architecture, applied ML, backend systems, evaluation, and production operations.
• Mentor engineers, collaborate with product teams, and turn emerging AI techniques into secure, reliable, scalable capabilities.

Your Responsibilities

• Lead the development of production-grade AI systems solving classification, extraction, reasoning, and automation problems.
• Build reusable GenAI platform components such as RAG systems, agent workflows, prompt systems, evaluation pipelines, document ingestion services, and APIs.
• Optimize ML and LLM-powered services for reliability, latency, observability, security, and cost efficiency.
• Drive improvements in classification, extraction, retrieval, and decisioning using modern AI techniques including embeddings and agent patterns.
• Mentor engineers, lead design reviews, and raise the team's execution quality.

Success Signals

In your first 12 months, you will deliver production-grade platform capabilities adopted by multiple teams, improve workloads' reliability and cost efficiency, establish reusable architecture patterns, enable faster onboarding of AI use cases, lead complex initiatives from ambiguity to delivery, and demonstrate clear impact as a technical mentor.

Qualifications

• B.S. in Computer Science or related field with 8+ years of experience in machine learning engineering, NLP, GenAI, information retrieval, or production AI systems.
• Strong software engineering skills in Python and backend system design.
• Hands-on experience shipping production ML/LLM systems (not just research prototypes).
• Mastery of modern AI techniques: RAG, embeddings, prompt engineering, classification, agent/tool-use patterns, SLMs.