Senior Software Engineer

Avalara•UNAVAILABLE, UNAVAILABLE

11d•Remote

About The Position

Avalara is an AI-first company where every engineer is expected to apply AI to improve speed, quality, automation, customer outcomes, and operational scale. AI is embedded into how we build products, operate services, and deliver customer value. Success at Avalara requires using AI as a core engineering capability. As a Software Development Engineer, Backend on the Returns team, you will help modernize and scale Avalara’s Returns-as-a-Service (RaaS) ingestion platform that powers tax return preparation, filing workflows, and emerging AI-driven Returns experiences. This role exists to accelerate Avalara’s ability to ingest, validate, normalize, and operationalize increasingly complex tax data at scale while improving reliability, automation, and customer trust. You will design and deliver scalable backend systems, APIs, ingestion pipelines, and operational tooling that improve filing accuracy, reduce operational friction, and enable faster onboarding of new data sources and filing capabilities. You will partner across engineering, product, operations, and platform teams to build systems that support Avalara’s long-term AI-first Returns strategy. This role elevates Avalara by improving platform scalability, operational efficiency, and customer outcomes while helping establish engineering and AI practices that raise the performance of the broader organization. Successful candidates will demonstrate Bar Raiser behaviors through strong ownership, engineering rigor, customer focus, and continuous improvement of systems, processes, and team capability. You will report to the Director of Engineering, Returns-as-a-Service Framework. This is a remote opportunity. #LI-Remote. This role is not eligible for visa sponsorship.

Requirements

B.S. in Computer Science or Engineering.
5+ years of relevant experience designing, building, and operating production-grade backend systems and distributed platforms.
Experience building scalable APIs, ingestion pipelines, data processing systems, and cloud-native services in modern engineering environments.
Strong experience with C#/.NET and modern software engineering practices including CI/CD, automated testing, secure development, and reliability engineering.
Experience operating workloads in AWS, including networking, IAM, observability, security, and cloud operational best practices.
Demonstrated ability to improve operational excellence through monitoring, troubleshooting, automation, scalability, and defect reduction initiatives.
Hands-on experience applying AI-assisted development tools and workflows to improve engineering speed, quality, automation, or operational effectiveness.
Experience integrating with or leveraging generative AI technologies such as OpenAI, Bedrock, Vertex, or Hugging Face, including practical understanding of prompt design, token optimization, rate limits, and retrieval-based patterns.
Strong ownership mindset with the ability to simplify complex systems, drive measurable outcomes, and improve the performance of teams and platforms over time.
Ability to demonstrate applied AI impact through measurable examples tied to engineering efficiency, automation, customer experience, quality improvement, or operational scale.
Demonstrated track record of using AI to drive measurable improvements in engineering productivity, software quality, operational efficiency, customer outcomes, or platform scalability.
Hands-on experience leveraging AI-assisted development tools and workflows across coding, testing, debugging, documentation, code review, operational analysis, and automation.
Experience integrating with or leveraging generative AI technologies such as OpenAI, Bedrock, Vertex, or Hugging Face, including practical understanding of prompt engineering, token optimization, rate limiting, evaluation approaches, and retrieval-augmented generation (RAG) patterns.
Ability to identify and implement AI-driven opportunities that reduce manual effort, accelerate delivery, improve data quality, strengthen operational intelligence, or enhance customer experiences.
Demonstrated judgment in applying AI responsibly, including awareness of data privacy, security, governance, reliability, and accuracy considerations in production systems.
Ability to quantify AI impact through measurable business or engineering outcomes such as reduced operational costs, faster issue resolution, improved development velocity, increased automation coverage, or higher platform reliability.
Proven ability to elevate the AI capabilities of peers and teams through mentorship, knowledge sharing, experimentation, and establishment of scalable AI engineering best practices.
Strong ownership mindset with the ability to simplify complex systems, drive measurable business outcomes, and consistently raise engineering standards across teams and platforms.
Demonstrated Bar Raiser behaviors through high accountability, strong technical judgment, continuous improvement, and a track record of leaving systems, processes, and teams stronger than they were found.

Responsibilities

Design, build, and operate scalable ingestion services that process and validate large-scale tax and returns data with high reliability, accuracy, and operational transparency.
Improve platform scalability, resiliency, and operational performance by reducing defects, strengthening observability, and improving monitoring and troubleshooting workflows.
Accelerate onboarding of new ingestion workflows and external data sources while improving SLA attainment, processing quality, and customer outcomes.
Contribute to architecture decisions, API design, secure-by-design engineering practices, automation strategies, and operational tooling that simplify complexity and improve long-term maintainability.
Apply AI-assisted engineering practices across software development, testing, operational analysis, documentation, troubleshooting, and workflow automation to improve engineering velocity and quality.
Identify opportunities to use AI to improve ingestion validation, anomaly detection, operational intelligence, and automated issue resolution at scale.
Partner cross-functionally with Returns, platform, operations, and product teams to deliver automation capabilities that improve filing accuracy, customer experience, and downstream Returns workflows.
Raise engineering standards through mentorship, technical leadership, code quality improvements, and reduction of technical debt.
Demonstrate Bar Raiser behaviors by improving systems, processes, execution quality, and the capabilities of those around you.
Contribute to measurable business outcomes including improved operational efficiency, reduced processing failures, faster onboarding of filing capabilities, and increased customer trust in Returns processing.