Jobs at Pave

Lead and grow an engineering team building core compensation planning and recommendation workflows. Drive technical roadmap for scalability, ensure calculation accuracy, partner with Product and Design, align with platform teams, and coach engineers while remaining hands-on with designs and implementations.

Build and ship product-facing features for Pave's compensation platform. Work cross-functionally with Product and Design to implement data-intensive web experiences, iterate rapidly based on customer feedback, and own work end-to-end. Contribute to frontend, backend, or full-stack development using modern web technologies.

Lead and build product-facing features for a compensation platform: own projects end-to-end, collaborate with Product and Design, mentor engineers, break down ambiguous problems, and ship data‑intensive web applications using modern technologies.

Build and maintain scalable data models and pipelines (especially Market Data) using dbt and Airflow; implement data observability, testing, and monitoring; collaborate with data scientists, product, and engineering to deliver production data products that drive revenue and customer value.

Build Pave's AI compensation analyst: design and implement agentic LLM systems that reason over proprietary compensation data and execute workflows. Collaborate with engineers and data scientists to ship end-to-end production features, architect retrieval and agentic pipelines, and drive product decisions with strong product instincts and ownership.

Lead complex enterprise sales cycles for Pave's compensation platform, selling six-figure deals to HR and Finance leaders. Build pipeline via inbound and outbound, partner with product, engineering, and customer success, influence roadmap, create account plans, and act as a trusted advisor to drive adoption and successful outcomes.

Manage the full sales cycle targeting HR and Finance leaders for a compensation SaaS platform. Drive consultative discovery, source new business, close deals, collaborate with product and customer success, provide market feedback, and help refine the sales playbook and go-to-market strategy.

Lead design and delivery of Pave's AI compensation analyst: build production LLM-based agentic systems that reason over compensation data, implement retrieval and evaluation frameworks, drive 0→1 technical initiatives, and align engineering and product to ship autonomous AI features into production.

Partner with Marketing to analyze Pave's compensation dataset and produce published data stories, reports, and customer-facing data products; field data questions from stakeholders; build lightweight measurement frameworks; and translate complex findings into clear narratives that drive product and business impact.

Own and build foundational backend services, APIs, and data infrastructure (auth, data models, pipelines) powering the compensation platform. Design generalized platform solutions, lead large ambiguous cross-team projects, and deliver scalable, maintainable systems.

Lead Developer Platform initiatives to improve developer experience and velocity. Define CI/CD and observability standards, rethink local workflows and incident response, integrate AI-assisted/agentic tooling, partner across product and data teams, own developer velocity and reliability metrics, and scale production systems.

Build and own foundational services, APIs, and data infrastructure for Pave's compensation platform. Implement authentication/authorization, data pipelines, and models; drive large cross-team initiatives with a focus on scalability, maintainability, and product-led platform solutions.

Own full-cycle technical recruiting for R&D (Engineering, Product, Design, Data): source passive candidates, partner with hiring managers, manage interviews and offers, leverage Pave compensation data to close candidates, run pipeline reporting in Greenhouse/Gem, and use AI tools to scale sourcing and outreach.

Drive end-to-end implementations of Pave's compensation platform for 30-40 customers, managing project plans, integrations, data migration, and stakeholder communication. Act as a trusted advisor to HR and Finance partners, surface product feedback, and collaborate with Product and Engineering to improve the platform and ensure customer adoption and outcomes.