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About This Role
The application window is expected to close on: 09/18/2026Job posting may be removed earlier if the position is filled or if a sufficient number of applications are received.
Meet the Team
Cisco Data Fabric is building the data foundation that connects, contextualizes, and activates machine data across hybrid environments. You will join a focused, highly collaborative team of product managers, engineers, architects, and AI and data specialists working across Splunk, Security, Observability, and IT. Our products transform telemetry, metadata, topology, and enterprise context into trusted knowledge that people and AI agents can reason over and act upon. We work closely with customers and internal product teams to solve complex data challenges at enterprise scale. This is an opportunity to help build an emerging knowledge graph platform at the center of Cisco’s data and AI strategy.
Your Impact
Own a significant product area within the AI Knowledge Graph, translating customer needs and business priorities into a clear roadmap, product requirements, and measurable outcomes. Define capabilities that connect telemetry, assets, topology, metadata, and business entities to power semantic search, contextual analytics, retrieval\-augmented generation, and agentic AI workflows. Partner with engineering and architecture to deliver graph schemas, ontology and metadata integrations, and scalable data\-processing and enrichment pipelines. Drive the product lifecycle from discovery and prioritization through launch, adoption, and continuous improvement. Measure success through customer value, adoption, quality of contextual results, platform usage, and reuse across Security, Observability, and IT experiences.
Minimum Qualifications
- Bachelor's Degree with 11\+ years of experience.
- 8\+ years of product management or equivalent technical product experience, including 3\+ years working on enterprise data, AI/ML, search, metadata, or platform products.
- Experience delivering an enterprise software or data\-platform capability from customer discovery and requirements definition through launch and adoption.
- Experience of knowledge graphs, graph databases, semantic models or ontologies, metadata systems, vector search, or retrieval\-augmented generation.
- Experience defining products that use distributed data\-processing technologies such as Apache Spark or comparable frameworks for large\-scale ingestion, transformation, and enrichment.
- Experience creating product requirements, roadmaps, data or API requirements, prioritization frameworks, and measurable success criteria in partnership with engineering teams.
Preferred Qualifications
- Experience with technologies such as Neo4j, Amazon Neptune, RDF/OWL, GraphRAG, LangChain, vector databases, or hybrid semantic search.
- Familiarity with agentic AI, Model Context Protocol, enterprise RAG systems, embedding pipelines, or federated data architectures.
- Product experience in observability, cybersecurity, IT operations, machine data, or enterprise data intelligence.
- Experience building platform capabilities used by multiple products, personas, or business domains across hybrid or multi\-cloud environments.
- Ability to lead customer discovery and align engineering, architecture, design, go\-to\-market, and partner teams around clear product outcomes.
Why Cisco?
At Cisco, we’re revolutionizing how data and infrastructure connect and protect organizations in the AI era – and beyond. We’ve been innovating fearlessly for 40 years to create solutions that power how humans and technology work together across the physical and digital worlds. These solutions provide customers with unparalleled security, visibility, and insights across the entire digital footprint.
Fueled by the depth and breadth of our technology, we experiment and create meaningful solutions. Add to that our worldwide network of doers and experts, and you’ll see that the opportunities to grow and build are limitless. We work as a team, collaborating with empathy to make really big things happen on a global scale. Because our solutions are everywhere, our impact is everywhere.
We are Cisco, and our power starts with you.
Message to applicants applying to work in the U.S. and/or Canada:
The starting salary range posted for this position is $179,000\.00 to $254,300\.00 and reflects the projected salary range for new hires in this position in U.S. and/or Canada locations, not including incentive compensation\*, equity, or benefits.
Individual pay is determined by the candidate's hiring location, market conditions, job\-related skillset, experience, qualifications, education, certifications, and/or training. The full salary range for certain locations is listed below. For locations not listed below, the recruiter can share more details about compensation for the role in your location during the hiring process.
U.S. employees are offered benefits, subject to Cisco’s plan eligibility rules, which include medical, dental and vision insurance, a 401(k) plan with a Cisco matching contribution, paid parental leave, short and long\-term disability coverage, and basic life insurance. Please see the Cisco careers site to discover more benefits and perks. Employees may be eligible to receive grants of Cisco restricted stock units, which vest following continued employment with Cisco for defined periods of time.
U.S. employees are eligible for paid time away as described below, subject to Cisco’s policies:
- 10 paid holidays per full calendar year, plus 1 floating holiday for non\-exempt employees
- 1 paid day off for employee’s birthday, paid year\-end holiday shutdown, and 4 paid days off for personal wellness determined by Cisco
- Non\-exempt employees\*\* receive 16 days of paid vacation time per full calendar year, accrued at rate of 4\.92 hours per pay period for full\-time employees
- Exempt employees participate in Cisco’s flexible vacation time off program, which has no defined limit on how much vacation time eligible employees may use (subject to availability and some business limitations)
- 80 hours of sick time off provided on hire date and each January 1st thereafter, and up to 80 hours of unused sick time carried forward from one calendar year to the next
- Additional paid time away may be requested to deal with critical or emergency issues for family members
- Optional 10 paid days per full calendar year to volunteer
For non\-sales roles, employees are also eligible to earn annual bonuses subject to Cisco’s policies.
Employees on sales plans earn performance\-based incentive pay on top of their base salary, which is split between quota and non\-quota components, subject to the applicable Cisco plan. For quota\-based incentive pay, Cisco typically pays as follows:
- .75% of incentive target for each 1% of revenue attainment up to 50% of quota;
- 1\.5% of incentive target for each 1% of attainment between 50% and 75%;
- 1% of incentive target for each 1% of attainment between 75% and 100%; and
- Once performance exceeds 100% attainment, incentive rates are at or above 1% for each 1% of attainment with no cap on incentive compensation.
For non\-quota\-based sales performance elements such as strategic sales objectives, Cisco may pay 0% up to 125% of target. Cisco sales plans do not have a minimum threshold of performance for sales incentive compensation to be paid.
The applicable full salary ranges for this position, by specific state, are listed below:
New York City Metro Area:
$194,600\.00 \- $328,600\.00
Non\-Metro New York state\& Washington state:
$179,000\.00 \- $294,000\.00
- For quota\-based sales roles on Cisco’s sales plan, the ranges provided in this posting include base pay and sales target incentive compensation combined.
\*\* Employees in Illinois, whether exempt or non\-exempt, will participate in a unique time off program to meet local requirements.
Salary Context
This $179K-$328K range is above the 75th percentile for AI Product Manager roles in our dataset (median: $185K across 167 roles with salary data).
View full AI Product Manager salary data →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 Cisco, 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. This role's midpoint ($253K) sits 17% above the category median. Disclosed range: $179K to $328K.
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.
Cisco AI Hiring
Cisco has 16 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, Research Engineer, AI Software Engineer. Positions span Seattle, WA, US, Milpitas, CA, US, San Jose, CA, US. Compensation range: $203K - $498K.
Location Context
AI roles in Seattle pay a median of $228,700 across 516 tracked positions. That's 6% above the national 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.
Frequently Asked Questions
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