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About This Role
AI Lead Product Manager
The AI Lead Product Manager will specialize in emerging AI (i.e., Agentic AI) and Machine Learning technology, and will also have a high level of AI/ML subject matter expertise.
Key Responsibilities and Duties
- Develop and execute a comprehensive product vision and strategy for generative AI use cases across various business departments like finance, fraud, legal etc.
- Collaborate with key stakeholders, including customers, engineers, designers, and data scientists to define product requirements and priorities
- Conduct market research and competitor analysis to identify emerging trends and opportunities in the generative AI space
- Create and present product roadmaps, business plans, and financial projections to senior leadership and stakeholders
- Ensure alignment with company goals, values, and brand identity through strategic planning and decision-making
- Play a critical part in the emerging technology program and assist in the design and ongoing development of innovative solutions to business issues using AI, Machine Learning and other emerging technologies.
- Leveraging expertise in product development, rapid prototyping, and user experience design to support the transformation to as Hybrid/Augmented AI Workforce era.
- Interface with internal and external TIAA stakeholders to understand how new and emerging technologies can affect their work and build functional prototypes to demonstrate the potential of future technologies
Recommend to business IT architectural patterns and solutions based on project goals and needs. *
Educational Requirements
- University (Degree)
Work Experience
- 5-7 years Required
- 7+ years Preferred
Physical Requirements
- Physical Requirements: Sedentary Work
Career Level
9IC
Additional Summary
The Emerging Tech Senior Specialist will specialize in emerging AI (i.e., Agentic AI) and Machine Learning technology, and will also have a high level of AI/ML subject matter expertise.
Additional Key Responsibilities and Duties
- Develop and execute a comprehensive product vision and strategy for generative AI use cases across various business departments like finance, fraud, legal etc.
- Collaborate with key stakeholders, including customers, engineers, designers, and data scientists to define product requirements and priorities
- Conduct market research and competitor analysis to identify emerging trends and opportunities in the generative AI space
- Create and present product roadmaps, business plans, and financial projections to senior leadership and stakeholders
- Ensure alignment with company goals, values, and brand identity through strategic planning and decision-making
- Play a critical part in the emerging technology program and assist in the design and ongoing development of innovative solutions to business issues using AI, Machine Learning and other emerging technologies.
- Leveraging expertise in product development, rapid prototyping, and user experience design to support the transformation to as Hybrid/Augmented AI Workforce era.
- Interface with internal and external TIAA stakeholders to understand how new and emerging technologies can affect their work and build functional prototypes to demonstrate the potential of future technologies
Recommend to business IT architectural patterns and solutions based on project goals and needs. *
Required Skills:
- 5+ Years of Product Management
- 3+ years in AI/ML
- Must have hands-on experience delivering Generative AI and Agentic AI products
- 5+ Years of experience in Agile Methodologies and Design thinking concepts
- Experience working directly with AWS Bedrock and AgentCore
Preferred Skills:
- Program Management experience
- Managing multiple stakeholders
- Good to have certification in PMP(Product Management Professional)
Related Skills Business Acumen, Data Preprocessing, Data Science, Innovation, Machine Learning, Market/Industry Dynamics, Predictive Modeling, Programming, Statistics Anticipated Posting End Date:
2026-10-07 Base Pay Range: $184,000/yr - $304,000/yr Actual base salary may vary based upon, but not limited to, relevant experience, time in role, base salary of internal peers, prior performance, business sector, and geographic location. In addition to base salary, the competitive compensation package may include, depending on the role, participation in an incentive program linked to performance (for example, annual discretionary incentive programs, non-annual sales incentive plans, or other non-annual incentive plans).
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Company Overview
Every worker deserves a secure retirement. For more than 100 years, TIAA has delivered it for millions of people. Founded to help educators retire with dignity, today weʼre a market-leading retirement company fueled by world-class asset management. But weʼre not just another legacy financial services firm. Weʼre fighting harder than ever before for our clients and the many Americans who need us.
Our Culture of Impact
At TIAA, we're on a mission to build on our 100+ year legacy of delivering for our clients while evolving to meet tomorrow's challenges. We equip our associates with future-focused skills and AI tools that enable us to advance our mission. Together, we are fighting to ensure a more secure financial future for all and for generations to come. We are guided by our values: Champion Our People, Be Client Obsessed, Lead with Integrity, Own It, and Win As One. They influence every decision we make and how we work together to serve our clients every day. We thrive in a collaborative in-office environment where teams work across organizational boundaries with shared purpose, accelerating innovation and delivering meaningful results. Our workplace brings together TIAA and Nuveen's entrepreneurial spirit, where we work hard and work together to create lasting impact. Here, every associate can grow through meaningful learning experiences and development pathways—because when our people succeed, our impact on clients' lives grows stronger.
Benefits and Total Rewards
The organization is committed to making financial well-being possible for its clients, and is equally committed to the well-being of our associates. That’s why we offer a comprehensive Total Rewards package designed to make a positive difference in the lives of our associates and their loved ones. Our benefits include a superior retirement program and highly competitive health, wellness and work life offerings that can help you achieve and maintain your best possible physical, emotional and financial well-being. To learn more about your benefits, please review our Benefits Summary .
Equal Opportunity
We are an Equal Opportunity Employer. TIAA does not discriminate against any candidate or employee on the basis of age, race, color, national origin, sex, religion, veteran status, disability, sexual orientation, gender identity, or any other legally protected status.
Our full EEO & Non-Discrimination statement is on our careers home page , and you can read more about your rights and view government notices.
Accessibility Support
TIAA offers support for those who need assistance with our online application process to provide an equal employment opportunity to all job seekers, including individuals with disabilities.
If you are a U.S. applicant and desire a reasonable accommodation to complete a job application please use one of the below options to contact our accessibility support team:
Phone: (800) 842-2755
Email: [email protected]
Drug and Smoking Policy
TIAA maintains a drug-free and smoke/free workplace.
Salary Context
This $184K-$304K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At TIAA, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($244K) sits 15% above the category median. Disclosed range: $184K to $304K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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: $272,250; VP: $235,000.
TIAA AI Hiring
TIAA has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Iselin, NJ, Dallas, TX, US. Compensation range: $304K - $304K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. 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).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). 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 (175) are outnumbered by mid-level (4,132) and senior (3,268) 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 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 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 $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. 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 (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 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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