Staff Product Manager (Evals)

Workato
Palo Alto, California$185k – $250kPosted 30 March 2026

Job Description

About Workato Workato transforms technology complexity into business opportunity. As the leader in enterprise orchestration, Workato helps businesses globally streamline operations by connecting data, processes, applications, and experiences. Its AI-powered platform enables teams to navigate complex workflows in real-time, driving efficiency and agility. Trusted by a community of 400,000 global customers, Workato empowers organizations of every size to unlock new value and lead in today’s fast-changing world. Learn how Workato helps businesses of all sizes achieve more at workato.com . Why join us? Ultimately, Workato believes in fostering a flexible, trust-oriented culture that empowers everyone to take full ownership of their roles . We are driven by innovation and looking for team players who want to actively build our company. But, we also believe in balancing productivity with self-care . That’s why we offer all of our employees a vibrant and dynamic work environment along with a multitude of benefits they can enjoy inside and outside of their work lives. If this sounds right up your alley, please submit an application. We look forward to getting to know you! Also, feel free to check out why: Business Insider named us an “enterprise startup to bet your career on” Forbes’ Cloud 100 recognized us as one of the top 100 private cloud companies in the world Deloitte Tech Fast 500 ranked us as the 17th fastest growing tech company in the Bay Area, and 96th in North America Quartz ranked us the #1 best company for remote workers Responsibilities We're looking for a Staff Product Manager to own evaluations for AI agents at Workato — both the internal framework that helps our teams ship better AI features, and the customer-facing tools that let builders assess and improve the agents they create. This is a role with a dual mandate. Internally, you'll establish how Workato evaluates agent quality, starting with Agent Studio and expanding to other teams shipping AI capabilities. Externally, you'll build the evaluation experience that helps business technologists understand why their agents succeed or fail — and what to do about it. The right person for this role has actually written evals. You've built test suites, designed evaluation criteria, and debugged agent failures in the trenches. You know the gap between "eval theory" and "eval reality," and you can translate that practitioner knowledge into products that work for both technical teams and non-technical builders. In this role, y ou will also be responsible to: Define and own the evaluation framework for Workato's 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 Qualifications / Experience 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 Experience driving adoption of frameworks or practices across engineering teams 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 underper ... (truncated, view full listing at source)
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