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Avalara

Cloud-based tax compliance and automation platform

Agentic and Machine Learning Engineer 5

AI Application Engineering +1Confirmed live in the last 24 hours
Full-timeUnited StatesNo salary listedIn-person

About the Job

Requirements

  • A B.S. in Computer Science, Engineering, or a closely related technical field is required.
  • At least 8 years of relevant professional experience building, deploying, and operating production software systems is required.
  • Hands-on experience building LLM applications, agentic systems, or AI-enabled workflows in production or production-like environments is required.
  • Experience with LLMs such as GPT, Claude, Llama, or similar models, including practical understanding of prompting, orchestration, evaluation, and reliability considerations, is required.
  • Experience with enterprise-scale software design, distributed systems, data structures, design patterns, and high-availability system operations is required.
  • Experience working in cloud computing environments such as AWS, Azure, or GCP is required.
  • Applied familiarity with modern agentic integration patterns and protocols such as MCP, A2A, tool use, retrieval, and multi-agent orchestration is required.
  • Demonstrated ability to use AI to improve measurable outcomes such as speed, quality, automation, insight, customer experience, or scale is required.
  • Strong communication, documentation, mentoring, and cross-functional collaboration skills are required.
  • Experience using AI and AI-related technologies is required, and the role requires applying AI to business challenges, staying current with AI trends and best practices, and sharing learnings.

Responsibilities

  • Design, build, and operate foundational agentic platform capabilities that enable Aviator, AAA, and other Avalara agentic experiences to move from prototype to production.
  • Develop scalable LLM application frameworks, orchestration patterns, tool integrations, and agent workflows that support enterprise-grade reliability, observability, security, and maintainability.
  • Create and improve evaluation methods for agentic outcomes, including quality, accuracy, latency, cost, safety, and task-completion effectiveness.
  • Translate ambiguous business and product needs into technical designs, prototypes, production features, and measurable engineering outcomes.
  • Apply modern software engineering practices, including CI/CD, automated testing, code review, documentation, and operational readiness, to ensure high-quality delivery.
  • Partner with product, engineering, security, data, and business stakeholders to ensure agentic capabilities solve meaningful customer and operational problems.
  • Research, assess, and responsibly apply emerging AI technologies, including LLMs, model-context protocols, agent-to-agent patterns, retrieval, evaluation, and automation techniques.
  • Document reusable patterns and implementation guidance that help Avalara engineers and software agents build consistently, safely, and efficiently.
  • Mentor engineers and raise the technical bar through design reviews, code reviews, coaching, and examples of high-ownership execution.
  • Strengthen the operational robustness of mature high-availability systems while introducing new AI capabilities without compromising customer trust or production stability.
  • Use AI tools and agentic workflows to improve engineering speed, design exploration, testing, code quality, documentation, debugging, operational analysis, and knowledge reuse.
  • Identify AI opportunities tied to measurable outcomes such as customer experience, workflow automation, developer productivity, reliability, cost efficiency, and risk reduction.
  • Apply AI responsibly, securely, and ethically, including attention to data handling, governance, evaluation, auditability, and failure modes.
  • Bring evidence to AI decisions, including impact metrics, tradeoff analysis, and clear reasoning for tool, model, protocol, or architecture choices.
  • Share AI practices, prompts, evaluation methods, implementation patterns, and lessons learned to raise the AI capability of peers and partner teams.

Desired Qualifications

  • Strong preference for Python experience.

About the Company

Avalara

Cloud-based tax compliance and automation platform

Avalara provides cloud-based tax compliance services for businesses of all sizes. Its main product, AvaTax, automatically calculates sales tax at the point of purchase and can be connected to many business apps to make tax determinations as orders move through a company’s systems. Beyond calculation, Avalara offers returns filing and ongoing compliance management, helping customers stay up to date with changing tax laws. The company earns money through subscriptions tied to how many transactions you process and how complex your tax needs are, and it also offers consulting and training services to support customers. Compared with others, Avalara covers many industries (like e-commerce, retail, manufacturing, and software) and integrates with a wide range of platforms, aiming to provide an end-to-end, automated tax workflow. Its goal is to reduce the risk of penalties and streamline tax compliance so businesses can focus on their core operations.

Company size
5,001-10,000
Company stage
IPO
Headquarters
Durham, NC
Founded
2004

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