Warp

• Decode federal, state, and local payroll tax notices, translate tax jargon into plain English, and guide founders through resolution via email, Zoom, and Pylon
• Draft letters, file responses, and represent Warp in conversations with IRS, state, and local tax agencies to resolve account discrepancies, penalties, and registration issues
• Process state tax registrations, assist with jurisdiction setup across all 50 states, and support quarterly filings (Form 941 and state equivalents)
• Maintain detailed case notes in Linear and Notion, track tax issues using project management platforms, and perform tax reconciliations
• Flag compliance trends and process gaps, turn recurring issues into FAQs and Founder Guides, and help improve workflows through automation initiatives
• Partner with Customer Support, Engineering, Product, and third-party administrators to test new tools and address client tax questions
• Assist with end of year preparation and other annual compliance processes

Tax Operations Specialist

Customer Support

• Own real-time customer engagement via chat, Slack, and email — maintaining high service quality and fast response times
• Resolve complex payroll challenges including tax notices and payment issues
• Develop deep product expertise in Warp and serve as the team's internal resource for how the platform works
• Partner with engineering on customer cases — context-setting, reproducing issues, and seeing them through to resolution rather than simply escalating
• Translate customer-reported issues into clear, well-documented technical briefs that engineering and product can act on
• Identify recurring or deep-rooted issues and bring them to leadership with recommendations — shifting from reactive resolution to proactive prevention
• Communicate with third-party partners integrated with Warp on behalf of customers
• Build and maintain a customer-facing knowledge base that evolves with product rollouts and common queries

Customer Experience Specialist

Implementation Specialist

• Lead implementation engagements end-to-end. Run our multi-week implementation support program — from kickoff (requirements gathering, use case selection, success criteria definition) through weekly implementation sessions to wrap-up (outcome review, ROI validation, expansion planning).
• Architect agent workflows with customers. Help enterprise teams design agent architectures for their specific use cases — selecting agent triggers, configuring sandboxed environments, and structuring multi-step agent workflows.
• Build alongside customers. Write and iterate on agent prompts, skills, and configurations. Set up environments, secrets, MCP servers, and integrations. Debug agent runs using session sharing and observability tools.
• Guide self-hosted deployments. For enterprises running agents on their own infrastructure, help configure self-hosted workers, Docker daemon connectivity, private registries, and task routing.
• Develop reusable implementation patterns. Document architecture patterns, reference implementations, and best practices that accelerate future deployments. Contribute to the Oz skill and template ecosystem.
• Be the voice of the customer to Product and Engineering. Surface implementation friction, feature gaps, and common pain points. Translate customer needs into actionable product feedback that shapes the Oz platform roadmap.
• Provide ongoing technical support. Respond to customer questions via Slack with same-business-day SLA. Troubleshoot agent failures, environment issues, and integration problems.

Implementation Engineer

Analytics Engineer

• Define, build, and visualize core product and business metrics
• Design and analyze A/B experiments to measure the impact of product changes
• Adapt data models to support new product features, pricing models, and business initiatives
• Partner with GTM teams to surface product-qualified leads, measure engagement, and support billing and revenue analytics
• Build and maintain canonical data models that serve as reliable sources of truth
• Write production ETL to ingest new data sources and reverse-ETL to push data to downstream consumers
• Develop AI agents to automate pieces of the data team’s workflows
• Establish data quality monitoring and reliability standards
• Build tooling that enables self-serve analytics across the company
• Conduct deep ad-hoc analysis to understand user behavior, model business mechanics, and uncover growth opportunities
• Dig into questions like: How does Warp expand within companies? What is the optimal pricing structure? How do we identify potential enterprise leads? What characteristics of AI interactions drive churn?