Staff Product Manager (Evals)

About the role

Workato delivers enterprise infrastructure for the agentic era, redefining iPaaS and helping enterprises unify data, applications, processes, and AI into a single, governed platform. A leader in Enterprise MCP and trusted by 50% of the Fortune 500, Workato’s cloud-native architecture connects every application, data source, and process to power real-time orchestration at scale. With enterprise-grade security and continuous innovation at its core, Workato provides the trusted foundation for organizations to automate with confidence and operationalize AI across the business.

Responsibilities

• Evaluate internal AI agent features, driving adoption across teams starting with Agent Studio
• Build the customer-facing evaluation experience — how builders test, measure, and improve agents they create on Workato
• Make hard calls about what evaluation complexity to expose versus abstract, balancing rigor with approachability
• Partner closely with the Build Experience PM to ensure evaluation is integrated into the builder journey, not bolted on
• Work with ML engineers and platform teams to ground the framework in technical reality while keeping it accessible
• Establish metrics for what "good" looks like — both for internal agent quality and for customer evaluation adoption
• Spend significant time with customers understanding where they struggle to assess agent performance and what mental models they bring

Requirements

• 7+ years in Product Management
• Hands-on experience writing evaluations for AI/ML systems (agents, LLMs, or similar)
• Track record of shipping technical products to both internal and external users
• Strong written and verbal communication skills
• Bachelor's degree or equivalent experience
• Practitioner depth in evaluations. You've written evals yourself — built test suites, designed rubrics, debugged why agents underperformed. You understand evaluation methodology not only from reading about it, but from doing it. You have opinions about what works, what doesn't, and where current approaches fall short.
• Strong product management experience. You've shipped products, driven roadmaps, and led cross-functional teams. You know how to translate technical capabilities into user value and write specs that don't leave details to chance.
• Technical translation ability. You can take complex evaluation concepts and make them accessible to business technologists without dumbing them down. You understand the difference between hiding complexity and organizing it.
• Internal influence skills. You've driven adoption of frameworks, practices, or tools across teams. You can be a credible partner to ML engineers while advocating for what internal teams actually need.
• Greenfield comfort. You've defined products from ambiguity — scoped v1s, made bets with incomplete information, and iterated based on what you learned. You don't need an existing playbook to be effective.
• B2B product sensibility. You see enterprise conventions as problems to solve, not constraints to accept. You're drawn to products that make complex workflows feel elegant.