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About This Role
*Together we fight for everyone’s opportunity for a better financial future.*
We will do this together — with customers, partners and colleagues. We will fight for others, not against: We will stand up for and champion everyone’s access to opportunities. The status quo is not good enough … we believe every individual and every community deserves access to financial opportunities. We are determined to support both individuals and communities in reaching a better financial future. We know that reaching this future depends on our actions today.
Like our Purpose Statement, Voya believes in being bold and committed to action. We are committed to a work environment where the differences that we are born with — and those we acquire throughout our lives — are understood, valued and intentionally pursued. We believe that our employees own our culture and have a responsibility to foster an environment where we all feel comfortable bringing our whole selves to work. Purposefully bringing our differences together to positively influence our culture, serve our clients and enrich our communities is essential to our vision.
Are you ready to join a company with a strong purpose and a winning culture? Start your Voyage –Apply Now
Voya Financial is hiring a Lead AI Product Manager to define and drive the AI product strategy for our Digital and Wealth Management business. This is a senior individual contributor role — you own the product vision, roadmap, and go\-to\-market execution for AI\-powered experiences that reach millions of retirement plan participants, thousands of financial advisors, and hundreds of plan sponsor clients.
This is not a coordination role. You are expected to be deeply technical — fluent in LLM architectures, agentic system design, and AI evaluation frameworks — while simultaneously holding the business judgment to decide which problems are worth solving in a fiduciary\-regulated environment. You write specs engineers actually use, evaluate prototype quality directly, and are accountable for whether shipped products change advisor behavior or improve participant retirement outcomes.
Lead\-level AI Product Managers at technology and financial services companies set product direction, make hard prioritization calls, and own outcomes — not activity. That is the bar for this role. Peer benchmarks: SVP AI PM at Citi, Sr. GenAI PM at Schwab AI.x, Staff AI PM at JPMorgan CDAO.
KEY RESPONSIBILITIES
Product Strategy \& Roadmap
- Define and maintain a 12–18 month AI product roadmap across participant engagement, advisor enablement, plan administration, and investment management — with clear prioritization rationale and measurable milestones.
- Identify and size AI opportunities using participant behavioral data, advisor workflow research, and competitive analysis; build business cases for leadership investment decisions.
- Define outcome\-oriented success metrics (retirement readiness improvement, advisor engagement rates, plan sponsor NPS, administrative cost reduction) and own accountability for those numbers post\-launch.
- Evaluate emerging AI capabilities — agentic frameworks, multimodal models, MCP integrations — and make principled decisions about when they are production\-ready for a regulated retirement context.
Discovery \& Specification
- Lead structured discovery with advisors, plan administrators, plan sponsors, and participants — translating complex financial domain problems into crisp product specs.
- Write PRDs and technical specifications that a small AI engineering team can execute against; define acceptance criteria that account for model quality thresholds, latency targets, and fiduciary output constraints.
- Maintain fluency in the participant retirement lifecycle: enrollment, contribution optimization, investment selection, pre\-retirement income planning, and decumulation — well enough to identify where AI can change outcomes at each stage.
Execution \& Delivery
- Partner daily with AI engineers; evaluate RAG pipeline quality, review agent behavior in staging, and make real\-time trade\-off calls throughout the build cycle.
- Drive rapid prototype cycles — working demo in days, not sprints — using working software to earn stakeholder trust faster than slide decks.
- Coordinate AI product launches with compliance, legal, and model risk teams; ensure AI outputs are structured to meet fiduciary standards and the 2026 interagency model risk management guidance (replaces SR 11\-7, effective April 2026\).
- Own go\-to\-market execution: advisor enablement materials, plan sponsor communication, participant rollout sequencing, and adoption measurement.
Stakeholder Alignment
- Build trusted relationships with senior stakeholders across Retirement, Investment Management, Distribution, Compliance, and Technology — influencing without authority.
- Communicate AI product strategy and trade\-offs clearly to audiences from engineers to C\-suite; written and verbal.
- Represent Voya’s AI product vision with plan sponsors, advisor partners, and industry forums where appropriate.
REQUIRED QUALIFICATIONS
Education
- Bachelor’s degree in Computer Science, Engineering, Finance, Economics, Mathematics, or a related quantitative discipline required.
- MBA or Master’s in a quantitative or financial field preferred. Equivalent professional experience considered.
Experience
- 8\+ years of product management experience, with at least 4 years in AI/ML product roles at a technology company, fintech, or financial services firm.
- Demonstrated track record of shipping AI\-powered products to production — owning the full lifecycle from discovery through measurable adoption.
- Lead or principal\-level experience: defined product strategy and roadmap independently, not just executed against someone else’s vision.
- Prior ownership of products in a regulated environment (financial services, healthcare, or similar); experience navigating compliance and legal review as part of the standard product process.
- Experience influencing VP\-and\-above stakeholders without direct authority.
AI \& Technical Fluency — Required and Evaluated
Evaluated rigorously. Candidates should expect to demonstrate these in the interview process, not just claim them on a resume.
- LLM product experience: shipped at least one production feature using large language models (OpenAI GPT\-4o, Anthropic Claude, Google Gemini, or equivalent); understands prompt engineering, system prompt design, context window management, and structured output extraction.
- RAG architecture fluency: can evaluate the quality of a RAG pipeline — chunking strategy, embedding model selection, retrieval precision/recall trade\-offs, re\-ranking logic, and hallucination mitigation. Does not need to implement but must be able to interrogate.
- Agentic AI product design: has designed or shipped features using agentic workflows (tool use, multi\-step reasoning, agent orchestration via LangChain, LangGraph, Vertex AI Agent Builder, Copilot Studio, or equivalent); understands where agents fail and how those failures affect fiduciary use cases specifically.
- Model evaluation and metrics: can define evaluation frameworks for AI outputs; understands precision/recall, ROC\-AUC, hallucination rates, and task\-specific quality metrics; able to review an LLM eval suite and assess whether it covers the right failure modes for a retirement context.
- Data fluency: comfortable interrogating SQL, reviewing data pipeline design, and forming hypotheses from participant behavioral data without requiring a data analyst to translate.
- AI tooling in practice: uses AI coding assistants (GitHub Copilot, Claude Code, Cursor, or equivalent) and agentic tools daily — this team builds with these tools, not about them.
- API and system awareness: can read a technical architecture diagram, understand latency/reliability constraints, and write specs that account for engineering realities including model serving costs and token limits.
- Experimentation: A/B test design, cohort analysis, statistical significance, and shadow deployment patterns for AI features in production.
Retirement \& Wealth Domain Knowledge — Required
Domain knowledge is a hard requirement. Assessed directly in the interview. Working fluency, not academic familiarity.
- Defined Contribution Plans: 401(k), 403(b), 457 mechanics; contribution limits and catch\-up provisions; employer match and vesting design; recordkeeper/TPA/plan sponsor ecosystem; QDIA rules; plan document fundamentals.
- ERISA \& Fiduciary Standards: ERISA prudence and loyalty requirements; functional fiduciary standard and prohibited transactions; how AI\-generated outputs must be structured to support — not replace — fiduciary decision\-making; DOL guidance on AI use in retirement plan contexts.
- 2026 Regulatory Landscape: SECURE 2\.0 provisions (auto\-enrollment, RMD changes, catch\-up rules); the April 2026 interagency model risk management guidance superseding SR 11\-7 — including its principles\-based approach to materiality tiering and proportional controls for AI and agentic systems; evolving DOL fiduciary rule.
- Participant Behavior \& Retirement Readiness: Behavioral finance drivers of savings inertia; retirement income adequacy frameworks; auto\-enrollment and escalation research; decumulation and guaranteed income strategies (relevant to SECURE 2\.0 lifetime income provisions).
- Investment Products: Target\-date fund construction and glide paths; managed account structures and fee models; model portfolio construction; how investment advice flows to participants in a qualified plan context.
- Advisor \& Plan Sponsor Dynamics: Advisor business models (RIA, broker\-dealer, captive); plan sponsor decision\-making and governance committee structures; competitive recordkeeper landscape; how AI advisor copilots are being deployed at Morgan Stanley, JPMorgan, and peer firms.
PREFERRED QUALIFICATIONS
- CFP, CFA (or candidate), CEBS, CRPS, or ASPPA credentials (QKA, QPA).
- Direct experience at a retirement recordkeeper, asset manager, RIA platform, or retirement\-focused fintech in a product or strategy role.
- Familiarity with the 2026 interagency model risk management framework and its practical application to GenAI and agentic systems in a regulated financial institution.
- Experience with voice\-of\-customer research at scale: in\-product feedback loops, NPS analysis, longitudinal participant cohort studies.
- Hands\-on experience with MCP (Model Context Protocol) integrations or multi\-agent system product design.
History of building 0* 1 AI products in an innovation lab or startup\-within\-a\-large\-institution context.
LEAD\-LEVEL EXPECTATIONS
At Voya, Lead is the senior IC level equivalent to Staff in engineering. Peer titles at comparable firms: SVP AI PM (Citi), Principal PM (Amazon), Staff PM (Stripe). These are the behaviors and outputs we hold Lead PMs accountable for:
Owns multi\-workstream AI product strategy
Sets measurable outcome goals, not activity goals
Prioritizes ruthlessly using data \+ domain judgment
Specs that need minimal iteration to execute against
Builds VP\+ trust autonomously across business units
Identifies compliance/risk exposure before escalation
Evaluates AI capability trade\-offs independently
Owns adoption metrics, not just launch dates
Coaches junior PMs; raises team hiring bar
Navigates 2026 MRM guidance as part of product process
Compensation Pay Disclosure:
Voya is committed to pay that’s fair and equitable, which means comparable pay for comparable roles and responsibilities.
The below annual base salary range reflects the expected hiring range(s) for this position in the location(s) listed. In addition to base salary, Voya offers incentive opportunities (i.e., annual cash incentives, sales incentives, and/or long\-term incentives) based on the role to reward the achievement of annual performance objectives. Please note that this salary information is solely for candidates hired to perform work within one of these locations, and refers to the amount Voya Financial is willing to pay at the time of this posting.
Actual compensation offered may vary from the posted salary range based upon the candidate’s geographic location, work experience, education, licensure requirements and/or skill level and will be finalized at the time of offer. Salaries for part\-time roles will be prorated based upon the agreed upon number of hours to be regularly worked.
$108,860 \- $136,080 USDBe Well. Stay Well.
Voya provides the resources that can make a difference in your lives. To us, this means thriving physically, financially, socially and emotionally. Voya benefits are designed to help you do just that. That’s why we offer an array of plans, programs, tools and resources with one goal in mind: To help you and your family be well and stay well.
What We Offer
- Health, dental, vision and life insurance plans
- 401(k) Savings plan – with generous company matching contributions (up to 6%)
- Voya Retirement Plan – employer paid cash balance retirement plan (4%)
- Tuition reimbursement up to $5,250/year
- Paid time off – including 20 days paid time off, nine paid company holidays and a flexible Diversity Celebration Day.
- Paid volunteer time — 40 hours per calendar year
Critical Skills
At Voya, we have identified the following critical skills which are key to success in our culture:
- Customer Focused: Passionate drive to delight our customers and offer unique solutions that deliver on their expectations.
- Critical Thinking: Thoughtful process of analyzing data and problem solving data to reach a well\-reasoned solution.
- Team Mentality: Partnering effectively to drive our culture and execute on our common goals.
- Business Acumen: Appreciation and understanding of the financial services industry in order to make sound business decisions.
- Learning Agility: Openness to new ways of thinking and acquiring new skills to retain a competitive advantage.
Equal Employment Opportunity
*Voya Financial is an equal\-opportunity employer. Voya Financial provides equal opportunity to qualified individuals regardless of race, color, sex, national origin, citizenship status, religion, age, disability, veteran status, creed, marital status, sexual orientation, gender identity, genetic information, or any other status protected by state or local law.*
Reasonable Accommodations
*Voya is committed to the inclusion of all qualified individuals. As part of this commitment, Voya will ensure that persons with disabilities are provided reasonable accommodations. If reasonable accommodation is needed to participate in the job application or interview process, to perform essential job functions, and/or to receive other benefits and privileges of employment,* *please* *reference* *resources for applicants with disabilities**.*
Salary Context
This $108K-$136K range is in the lower quartile for AI Product Manager roles in our dataset (median: $189K across 161 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 3,823 AI roles we're tracking, AI Product Manager positions make up 5% of the market. At Voya Financial, 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 $213,800 based on 583 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($122K) sits 43% below the category median. Disclosed range: $108K to $136K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Voya Financial AI Hiring
Voya Financial has 2 open AI roles right now. They're hiring across AI Product Manager, AI/ML Engineer. Positions span Philadelphia, PA, US, New York, NY, US. Compensation range: $136K - $190K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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