Interested in this AI Product Manager role at Talkdesk?
Apply Now →Skills & Technologies
About This Role
We are hiring a Forward Deployed AI Product Manager (FDPM) to be the connective tissue between our clients and our engineering teams. This is not a traditional PM role. You will live at the intersection of product strategy, technical delivery, and client partnership—spending meaningful time embedded with global retailers, e-commerce brands, hotel chains, and restaurant operators to deeply understand their workflows, then translating those insights into AI-powered solutions that ship. Think of this as: part Product Manager, part Implementation Consultant, part Pre/Post-Sales Engineer. Whether you have run operations inside a retailer, hotel group, or restaurant brand, implemented enterprise software at an e-commerce, POS, PMS, or loyalty vendor, or advised retail and hospitality clients as a consultant—if you have felt the pull toward product and AI, this role was built for you.
What You'll Do: Client Engagement & Discovery
- Trusted Advisory: Serve as the primary Product point of contact for a portfolio of retail, e-commerce, and hospitality clients, owning relationships across contact center operations, guest experience, digital commerce, IT, and store operations stakeholders, often working alongside C-suite sponsors.
- Workflow Observation: Lead structured discovery engagements—interviews, order journey mapping, queue observation, side-by-sides with agents, and document analysis—to surface automation opportunities and unmet guest, shopper, or operational needs.
- The "Art of the Possible": Connect client operational pain points to Talkdesk AI platform capabilities—while respecting brand voice, consumer privacy, and PCI constraints.
Solution Design & Scoping
- Scope Alignment: Review pre-sales Statements of Work drafted by the Solution Architect to confirm scope, feasibility, and design alignment before signature.
- Workflow Translation: Translate real-world workflows—WISMO (Where Is My Order?), returns, exchanges, loyalty/rewards inquiries, reservation bookings, cancellations, check-in flows, and VIP guest routing—into structured product and configuration requirements with FDEs, Applied ML, and Solution Engineers.
- Specification Ownership: Own the quality and completeness of Autopilot design and specification documents for engineering handoffs, including required brand disclosures, payment verification steps, TCPA consent capture, and agent escalation criteria.
- Success Metrics Definition: Define business KPIs with clients (time-to-value, containment/call deflection, self-service completion rate, first-contact resolution, CSAT/NPS, average handle time, order modification cycle time, cost per contact).
- Scope Discipline: Enforce scope discipline during UAT—route out-of-scope requests to the Sales/Account team to follow formal change order processes.
Pilots, Deployment & Value Delivery
- 01 Executions: Own deployments end-to-end (from pilot kickoff through go-live): manage timelines, stakeholder communication, change management, and the iteration cycle to production-ready deployments.
- Validation & Testing: Generate test criteria from build artifacts and conduct manual validation (e.g., live call testing, messaging flow validation, authentication paths) before approving client UAT.
- Compliance & Security: Partner with client legal, privacy, and security teams to clear control requirements (PCI DSS, TCPA, GDPR/CCPA) gating go-live.
- On-Site Go-Live: Drive client UAT to completion efficiently, including on-site travel for strategic accounts to accelerate testing and fine-tuning.
- Post-Launch Observability: Monitor deployed Autopilots post-launch using observability tooling, evaluating performance and resolving field issues.
- Value Handoff: Ensure clients and Customer Experience Managers (CXMs) can successfully measure business success metrics before handing off ongoing account management.
- ROI Narratives: Document ROI proof points and case-for-scale narratives at go-live to empower CXMs to drive expansion and renewal.
Product Feedback & Roadmap Influence
- Field Insights: Surface reusable patterns and high-priority gaps from retail/hospitality field deployments to the core product roadmap, partnering with engineering and product leadership.
- Scale the Practice: Contribute to internal knowledge bases, playbooks, and templates that systematize the FDPM practice as Talkdesk scales.
Required Qualifications
- Industry Context: 5+ years of experience in retail, e-commerce, or hospitality, with hands-on exposure to customer-facing or back-office operations in one or more domains: Retail & E-Commerce Servicing (orders, returns, exchanges, promotions, loyalty), Hospitality Operations (reservations, guest relations, front desk, concierge), or Restaurant Operations (ordering, delivery support, table management).
- Client Delivery Ownership: Demonstrated experience in a client-facing delivery role—implementation, technical project management, solution consulting, or customer success—where you owned outcomes, not just activities.
- Workflow Mapping: Ability to quickly understand and map complex guest and shopper journeys, then distill them into structured requirements and prioritized problem statements.
- Executive Communication: Strong written and verbal communication skills; able to present to VP/C-suite stakeholders and translate between store/guest operations and technical teams.
- Regulatory & Data Familiarity: Working familiarity with TCPA outbound contact rules, PCI DSS payment handling, consumer data privacy regulations (CCPA, GDPR), and brand compliance guidelines.
- Comfort with Ambiguity: Comfortable operating in whitespace; you default to action and bring structure to uncertainty.
- AI & Agentic Curiosity: Experience with agentic workflows and a strong curiosity about AI and automation; eager to develop deep product fluency in conversational AI for guest and retail experiences.
- Travel: Willingness to travel up to 25–35% to client sites for discovery, UAT, go-live support, and executive meetings.
Preferred Qualifications
- Vendor Experience: Experience at a retail, e-commerce, OMS, POS, PMS, or loyalty technology vendor in a Project Manager, Implementation Consultant, Technical Services, or similar role.
- High-Growth Expertise: Prior experience in a startup or high-growth technology company in a PM, solutions engineering, or FDE-adjacent capacity.
- Contact Center Ecosystems: Familiarity with CCaaS, IVR, or conversational AI, with prior exposure to platforms such as Talkdesk, Genesys, Five9, or similar.
- Integrations & Standards: Understanding of APIs and integrations with retail/hospitality platforms—Shopify, Salesforce Commerce Cloud, Magento, SAP, Oracle Commerce, Workday, Opera PMS, Sabre, Amadeus, or major loyalty and POS systems.
- Agile Product Delivery: Experience building or contributing to product roadmaps, writing PRDs, or working in Agile/scrum delivery processes.
Pay Range (Base Pay): $200,000 - $240,000
Other Types of Pay: Based on level and role the employee may be eligible for long term incentives in the form of equity and short term incentives of either bonus or commission.
Health Insurance: Medical, Dental, Vision, Life and Disability Insurance, Employee Assistance Program (EAP).
Retirement Benefits: 401(k) plan
Paid Time Off: Talkdesk offers an uncapped paid time off program for exempt employees and an accrual-based program for non-exempt employees; both are subject to manager approval and consistent with business needs.
Paid Holidays: Talkdesk offers 14 paid holidays each year.
Paid Sick Leave: Exempt employees have uncapped paid time off and non-exempt sick leave follows accrual standards; both are subject to manager approval and consistent with business needs.
Method of Application: Apply online.
Application Window: The application window is expected to close at least 60 days from the posting date. The application was posted on 09/23/2026
Benefits and perks listed above may vary based on the nature of your employment with Talkdesk.
All questions or concerns about this posting should be directed to the Talent team at [email protected].
Talkdesk is pioneering a new era of Customer Experience Automation (CXA), redefining how the world's most admired brands interact with their customers through AI. Our global team of courageous innovators is customer-obsessed, building AI-first solutions that put empathy, trust, and transparency at the center of every interaction. We foster an inclusive culture where diverse perspectives drive our success and every voice belongs. Combining the stability of a global leader with the agility of a disruptor, Talkdeskers are empowered with the autonomy to drive meaningful impact, while giving back to the communities and environment around us.
Talkdesk has been recognized as a Leader in the Gartner® Magic Quadrant™ for Contact Center as a Service (CCaaS) and in the G2 Overall Grid® Reports for AI Agents and Contact Center. With seven consecutive years on the Forbes Cloud 100 and multiple AI Breakthrough awards, there has never been a more exciting time to join us as we shape the future of customer experience automation!
Work Environment and Physical Requirements:
Primarily office-environment work, extended periods of sitting or standing, computer-based work. Limited lifting, and equipment usage limited to computer-related equipment (keyboards, mouse, etc.)
The Talkdesk story hinges on empathy and acceptance. It is the shared goal among all Talkdeskers to empower a new kind of customer hero through our innovative software solution, and we firmly believe that the best path to success for our mission is inclusivity, diversity, and genuine acceptance. To that end, we will hire, promote, work along, cheer for, bond with, and warmly welcome into the Talkdesk family all persons without regard to ethnic and racial identity, indigenous heritage, national origin, religion, gender, gender identity, gender expression, sexual orientation, age, disability, marital status, veteran status, genetic information, or any other legally protected status.
Salary Context
This $200K-$240K range is above the 75th percentile for AI Product Manager roles in our dataset (median: $193K across 99 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 9,653 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At Talkdesk, 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 $220,200 based on 862 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. Disclosed range: $200K to $240K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Talkdesk AI Hiring
Talkdesk has 1 open AI role right now. They're hiring across AI Product Manager. Based in Remote, US. Compensation range: $240K - $240K.
Remote Work Context
Remote AI roles pay a median of $185,906 across 2,124 positions. About 6% of all AI roles offer remote work.
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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.