• Work closely with the whole company to plan, design, build, and refine core product features. • Own user-impacting features from conception to completion. • Have significant ownership in our technical architecture and product roadmap. • Work in a variety of languages including Rust, Go, Typescript, and Haskell.

• You'll design and build the systems that run Treewalk's business — internal tools, integrations, and workflow automations — using AI coding tools to accelerate delivery without sacrificing quality • You'll operate as a hands-on builder who starts from accounting processes and business operations, translates them into clear technical specifications, and ships robust, production-grade solutions. • You will own the technical direction of our product stack: architecture decisions, security posture, code quality standards, and how we scale responsibly as these tools move from internal use to client-facing deployments. • In this role you'll regularly navigate ambiguous problem spaces and serve as the technical anchor for AI-native software development practices at Treewalk. • You'll help define what the future of accounting work looks like — through thoughtful, responsible development of a finance and accounting suite of tools built on modern AI infrastructure. • Architect, build, and iterate on internal AI-powered tools — invoice processing, accrual workflows, document classification, client reporting, and more. • Write production-quality code and set the technical bar for how the team builds, reviews, and ships software. • Partner with internal accounting staff to understand process pain points and translate them into clean system designs. • Define and enforce security, data handling, and access control standards as tools expand to client environments. • Own architectural decisions: service design, data models, API contracts, queue/worker patterns, cloud infrastructure. • Establish code quality standards and review practices, including responsible use of AI-generated code. • Identify and mitigate technical risk proactively — especially as systems handle sensitive financial data. • Document systems clearly so that non-engineers can understand what's been built and why. • Work closely with the internal product lead (non-engineering background) to bridge business requirements and technical execution. • Surface trade-offs clearly and make pragmatic calls under ambiguity. • Contribute to hiring and capability-building as the team grows.

• Own delivery of frontend and backend features, from technical design through production release and ongoing iteration. • Build and maintain performant, accessible UIs and robust backend services, enabling seamless AI-powered interactions for end users. • Work closely with product managers, designers, and ML engineers to translate requirements into impactful technical solutions. • Participate in architectural decisions within your team and influence technical direction across teams as experience grows. • Proactively identify and address technical debt, ensuring high performance, reliability, and maintainability. • Drive adoption and extension of Zendesk’s design system and component library for frontend consistency. • Writing backend business logic, optimizing queries, and ensuring system performance and scalability. • Ensuring quality through backend-focused testing, such as integration and API contract tests. • Mentor junior engineers through code review, pairing, and knowledge-sharing sessions.

• Define and drive technical strategy for major product areas, ensuring long-term scalability, reliability, and developer velocity • Lead large, cross-team initiatives from concept through launch and iteration (including sequencing, dependency management, and stakeholder alignment) • Architect and evolve complex systems spanning frontend, backend services, and data models; make pragmatic tradeoffs that optimize for customer outcomes and operational excellence • Establish and evolve engineering standards (architecture patterns, testing strategy, performance budgets, reliability/observability practices) that scale across teams • Build AI-powered experiences using LLMs, embeddings, and related techniques where they create real user value, with strong engineering around evaluation, observability, and iteration • Proactively identify and eliminate systemic risks: reliability gaps, performance bottlenecks, scaling constraints, and operational toil • Mentor and develop engineers across levels; amplify impact by coaching technical leaders and raising the bar for design and execution • Communicate clearly and influence effectively across engineering, product, design, and leadership—especially during high-ambiguity decisions