Senior Product Manager (Scale Test)

Join Salesforce as a Senior Product Manager for Scale Test, a high-impact role at the intersection of platform engineering, enterprise customer success, and AI-driven testing innovation. You will be the strategic owner of the Scale Test product, deeply understanding customer needs and driving feature development. You will also own the prioritized roadmap, end-to-end product strategy, and delivery across multiple engineering teams. Additionally, you will define success metrics, represent Scale Test at customer conversations, and collaborate with various teams for go-to-market strategies.

• Servir como el principal punto de contacto para los clientes empresariales que utilizan Scale Test, trabajando en estrecha colaboración con ellos para recopilar comentarios técnicos.
• Asumir la propiedad estratégica del producto, comprendiendo profundamente cómo los clientes más grandes arquitectan y prueban sus implementaciones de Salesforce.
• Definir y dirigir la estrategia de pruebas de Agentforce, comprendiendo cómo los clientes construyen y validan las implementaciones de Agentforce a gran escala.
• Experience with testing/observability/monitoring products — you've built or managed products in the DevOps toolchain (load testing, APM, observability, CI/CD, test automation)
• 7+ years of Product Management experience, with at least 4+ years in a B2B SaaS or platform product role
• Bachelor's degree in Computer Science, Engineering, or technical field; MBA or Master's degree in Business, Product Management, or related field a plus
• Track record of working directly with enterprise customers to surface insights and drive product improvements — ideally in performance-sensitive or platform contexts
• Deep technical fluency across performance testing, load generation, cloud infrastructure (AWS, Kubernetes), and SaaS architecture — able to engage deeply with engineering teams and communicate clearly to executive audiences
• Demonstrated experience owning complex, technically deep products end-to-end — from discovery through launch and iteration
• Ability to operate autonomously in ambiguous, fast-moving environments and make high-quality decisions with incomplete information
• Agentic testing experience — you've built or managed products that test AI agents, agentic workflows, or autonomous systems; you understand how to validate quality, reliability, and performance for non-deterministic AI systems
• Prior experience with performance testing, load testing, or developer tooling products (e.g., LoadRunner, JMeter, or similar)
• Excellent communication and storytelling skills — equally comfortable in an engineering sprint review, a customer-facing demo, and an executive business review
• Strong technical fluency — ability to engage meaningfully with engineering on infrastructure, load generation, APIs, and system design without needing to code
• Hands-on exposure to Agentforce, Hyperforce, Salesforce Platform, or adjacent Salesforce products
• Deep knowledge of Salesforce platform — multi-tenant SaaS architecture, sandboxes (Full Copy, Partial Copy, Developer), Hyperforce, governor limits, metadata model, Apex/LWC performance
• Experience with Salesforce developer tooling or platform products — DevOps Center, Code Analyzer, Optimizer, CLI, VS Code extensions, or similar DX products
• Understanding of Agentforce testing needs — what metrics matter (prompt latency, action execution time, LLM error rates, agent success rate, reasoning quality), how to measure quality at scale
• Experience working with Partners (ISVs / SIs) and managing partner-driven product requirements
• Familiarity with GovCloud / public sector requirements including FedRamp or equivalent data residency standards
• Passion for democratizing developer tooling — making complex workflows accessible to non-engineers through low-code and AI-guided experiences (Vibe Testing)
• BS or MS in Computer Science, Engineering, or a related technical field (or equivalent experience)
• Experience driving Pilot-to-GA product launches with strong customer feedback loops