Machine Learning Engineer V

Avalara is accelerating its AI-first transformation by building intelligent systems that automate complex compliance workflows, understand unstructured and structured data, and enable product teams to deliver AI-powered experiences at scale. As Avalara works toward its ambition of being part of every transaction in the world, we are investing in senior ML leadership to build the next generation of production AI capabilities.

We are looking for a Machine Learning Engineer V to lead technically complex AI and ML initiatives across classification, document intelligence, GenAI platforms, retrieval, agentic workflows, and production ML systems. This role is for an engineer who can operate across architecture, applied ML, backend systems, evaluation, and production operations.
The ideal candidate is not just a model builder. They can own systems end to end, make strong architectural decisions, mentor engineers, collaborate with product and domain experts, and turn emerging AI techniques into secure, reliable, scalable product capabilities.

This role helps Avalara build high-leverage AI capabilities that can be reused across many teams, products, and workflows. Senior ML engineers in this space will shape how Avalara applies GenAI, ML classification, document intelligence, retrieval, and agentic systems to improve automation, quality, and scale.

This engineer will help Avalara:

• →Lead development of production AI systems that solve complex classification, extraction, reasoning, and automation problems
• →Build reusable GenAI and ML platform capabilities that help product teams ship AI features faster
• →Advance state-of-the-art document intelligence capabilities for ingestion, extraction, validation, and structured understanding
• →Improve the quality, reliability, observability, security, and cost efficiency of LLM and ML workloads
• →Establish stronger evaluation, governance, and production-readiness standards for AI systems
• →Raise the technical bar across architecture, system design, mentoring, and operational ownership 

• →Lead architecture and delivery of production-grade AI and ML systems across classification, document intelligence, retrieval, and GenAI platform capabilities
• →Design and scale GenAI platform components such as RAG systems, agent workflows, prompt systems, evaluation pipelines, document ingestion services, and developer-facing APIs
• →Build and optimize ML and LLM-powered services with strong reliability, latency, observability, security, and cost controls
• →Work across model providers and model families to optimize model selection, routing, latency, quality, and spend
• →Develop reusable SDKs, APIs, workflows, and platform components that enable teams to ship AI features faster
• →Lead evaluation and governance mechanisms for AI quality, grounding, safety, explainability, compliance, and production risk management
• →Drive improvements to classification, extraction, retrieval, and decisioning systems using ML, SLMs, LLMs, embeddings, and agent/tool-use patterns
• →Partner with product, application teams, infrastructure, security, and domain experts to onboard new use cases and data sources
• →Mentor engineers, lead design reviews, improve engineering standards, and raise the team’s execution quality 

Success in the first 12 months would include:

• Delivered production-grade AI platform or ML capabilities adopted by multiple product or engineering teams
• Improved quality, reliability, latency, cost efficiency, or observability of important ML or LLM workloads
• Established reusable architecture patterns for GenAI, classification, document intelligence, evaluation, or model serving
• Enabled faster onboarding of new AI use cases, data sources, or product workflows
• Improved governance and evaluation standards for AI-powered systems
• Led at least one technically complex AI initiative from ambiguity through production delivery
• Demonstrated clear impact as a technical mentor and raised the quality of architecture, design reviews, implementation, and operational ownership 

This role must operate as an AI Bar Raiser. The engineer should:

• Apply advanced AI techniques such as LLMs, SLMs, RAG, embeddings, transformer models, agentic workflows, tool use, and document intelligence
• Use AI to materially improve speed, quality, automation, insight, scale, reliability, or cost efficiency
• Make strong architectural tradeoffs across accuracy, latency, reliability, security, governance, and spend
• Design production-grade evaluation systems for AI quality, grounding, regression testing, safety, and failure analysis
• Identify meaningful AI opportunities tied to product value, operational efficiency, customer experience, and risk reduction
• Share patterns, tools, and best practices that elevate AI capability across teams
• Apply AI responsibly, securely, and ethically in enterprise-grade production systems 

We are looking for engineers who raise the technical quality and production maturity of AI and ML systems around them. In this role, that means demonstrating strong ownership, sound judgment, and measurable impact across model development, evaluation, deployment, and ongoing improvement. The ideal candidate uses data, experiments, and production signals to guide technical decisions; understands the tradeoffs between accuracy, latency, reliability, cost, and maintainability; and brings rigor to how models are evaluated, monitored, and improved over time. They help raise the team’s AI/ML capability by improving experimentation practices, model evaluation standards, code quality, documentation, observability, and operational readiness. They also contribute reusable patterns that make future AI systems easier to build, safer to operate, and more scalable in production.

• B.S. in Computer Science, Engineering, or related technical field
• 8+ years of experience in machine learning engineering, applied ML, NLP, GenAI, information retrieval, or production AI systems
• Strong software engineering skills in Python and experience designing backend services, APIs, distributed systems, or cloud-native platforms
• Experience shipping production ML or LLM systems, not just experimentation or research prototypes
• Hands-on experience with modern AI techniques such as RAG, embeddings, prompt engineering, evaluation, classification, agent/tool-use patterns, SLMs, or LLM-powered workflows
• Experience with cloud-native systems, APIs, async or distributed workflows, observability, monitoring, and production operations
• Ability to design for security, access control, governance, compliance, reliability, and cost efficiency
• Strong system design skills and ability to lead technically complex initiatives across multiple stakeholders
• Ability to mentor engineers and raise technical standards across teams 

• Experience with multi-model gateways, model routing, model selection, or model cost optimization
• Experience with document AI, OCR, extraction, layout understanding, or validation workflows
• Experience with evaluation frameworks for LLMs, agents, classification systems, or retrieval systems
• Experience building internal AI platforms, developer tooling, SDKs, self-serve portals, or shared ML infrastructure
• Experience with Kubernetes, Docker, MLflow, Terraform, AWS, GitLab, Postgres, Prometheus, or Grafana
• Experience in domains with complex taxonomies, compliance requirements, long-tail data, or high reliability expectations

We’re defining the relationship between tax and tech.

We’ve already built an industry-leading cloud compliance platform, processing over 54 billion customer API calls and over 6.6 million tax returns a year. Our growth is real - we're a billion dollar business - and we’re not slowing down until we’ve achieved our mission - to be part of every transaction in the world.

We’re bright, innovative, and disruptive, like the orange we love to wear. It captures our quirky spirit and optimistic mindset. It shows off the culture we’ve designed, that empowers our people to win. We’ve been different from day one. Join us, and your career will be too.

AI is embedded in our workflows, decision-making, and products.  Success here requires embracing AI as an essential capability.

• You’ll bring experience using AI and AI-related technologies, ready to thrive here.

• You’ll apply AI every day to business challenges - improving efficiency, contributing solutions, and driving results for your team, our company, and our customers.

• You’ll grow with AI by staying curious about new trends and best practices, and by sharing what you learn so others can benefit too.

Total Rewards 

In addition to a great compensation package, paid time off, and paid parental leave, many Avalara employees are eligible for bonuses. 

Health & Wellness 
Benefits vary by location but generally include private medical, life, and disability insurance. 

Inclusive culture and diversity 
Avalara strongly supports diversity, equity, and inclusion, and is committed to integrating them into our business practices and our organizational culture. We also have a total of 8 employee-run resource groups, each with senior leadership and exec sponsorship. 

Supporting diversity and inclusion is a cornerstone of our company — we don’t want people to fit into our culture, but to enrich it. All qualified candidates will receive consideration for employment without regard to race, color, creed, religion, age, gender, national orientation, disability, sexual orientation, US Veteran status, or any other factor protected by law. If you require any reasonable adjustments during the recruitment process, please let us know.