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About This Role
About Workato
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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. To learn more, visit www.workato.com
Why join us?
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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
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The Model Context Protocol (MCP) is rapidly becoming the standard for how AI agents connect to enterprise systems — and Workato is building the gateway, governance, and runtime layer that makes MCP safe and scalable for the largest enterprises in the world. We are looking for a Senior Product Manager to own critical pieces of our Enterprise MCP roadmap and help shape how thousands of customers expose their tools, data, and workflows to AI agents.
You'll work at the intersection of API management, identity, and AI infrastructure. Your decisions will shape how AI agents authenticate, what they're allowed to do, and how enterprises observe and govern agent behavior across hundreds of MCP servers and millions of tool invocations.
This is a builder role on a small, senior team. You'll write PRDs that go straight into engineering, run customer design partners, present to analysts, and partner with GTM to land enterprise deals. If you've been waiting for the right moment to go deep on agentic infrastructure with the API and gateway chops you've built — this is it.
In this role, you will also be responsible to:
- MCP Gateway capabilities — Drive the roadmap for one or more pillars of our gateway: authentication and identity (OAuth, OIDC, DCR, M2M), policy enforcement, observability, or runtime performance.
- MCP server lifecycle — Define how MCP servers are designed, published, versioned, and governed across customer environments. Set quality bars and develop the frameworks (tiering, certification, server design best practices) that scale across the ecosystem.
- Enterprise readiness — Translate enterprise security, compliance, and audit requirements into shippable product. Work directly with CISO and platform teams at design\-partner customers.
- Cross\-functional leadership — Partner with engineering leads on architecture decisions, with GTM on positioning and enablement, and with analyst relations on category\-defining narratives (Gartner, Forrester).
- Competitive and market intelligence — Maintain a sharp point of view on the MCP landscape — OSS projects, hyperscaler offerings, and competing platforms — and translate that into product bets and differentiation.
Requirements
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### Qualifications / Experience / Technical Skills
- 4–5 years of product management experience, with at least 2 years on API platforms, API gateways, integration platforms, or developer infrastructure.
- Deep technical fluency in API management and gateway architecture — you understand the difference between an API gateway, an ingress controller, and a service mesh, and can hold your own in design reviews on rate limiting, routing, policy enforcement, and observability.
- Working knowledge of OAuth 2\.0, OIDC, and modern identity patterns (authorization code flow, client credentials, DCR, token exchange). You can read an RFC and tell engineering whether the proposed design is spec\-compliant.
- Track record of shipping technical products to enterprise customers, including writing PRDs that engineers respect and running design partner programs.
- Strong written communication — you can move fluently between a CISO briefing, a developer\-facing blog post, and a six\-page strategy doc.
### Strongly Preferred
- Experience with AI Gateway, LLM proxy, or agent runtime products (e.g., LiteLLM, Portkey, Kong AI Gateway, or in\-house equivalents). Bonus if you've thought hard about prompt injection, tool poisoning, or agent identity.
- Hands\-on familiarity with MCP as a spec — you've built an MCP server, integrated one, or have strong opinions about transports, resources vs. tools, and elicitation.
### Nice to Haves
- Prior experience at an iPaaS, API management vendor, or AI infrastructure company.
- Analyst relations experience (Gartner MQ, Forrester Wave).
- Conference speaking or technical writing presence in the API or AI infra communities.
(REQ ID: 2925\)
Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 4,317 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At Workato, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $217,100 based on 471 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Workato AI Hiring
Workato has 1 open AI role right now. They're hiring across AI Product Manager. Based in Palo Alto, CA, US.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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