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About This Role
About Rockefeller Capital Management:
Rockefeller Capital Management was established in 2018 as a leading independent financial advisory services firm. Originally founded in 1882 as the family office of John D. Rockefeller, the Firm has evolved to offer strategic advice to ultra\- and high\-net\-worth individuals and families, institutions, and corporations from offices in 33 markets throughout the United States, as well as an office in London. The Firm oversees $221 billion in client assets as of May 31, 2026\.
Position:
The Vice President, Project Manager, AI Initiatives will support the planning, execution, governance, and adoption of firmwide artificial intelligence initiatives across Rockefeller Capital Management. Reporting into the Technology Program Management Office, this role will partner closely with business, technology, information security, legal, compliance, risk, data, and vendor teams to move firmwide AI opportunities from intake and assessment through controlled delivery, launch, measurement, and ongoing governance. The ideal candidate combines strong project management discipline, practical familiarity with AI\-enabled solutions, executive\-level communication skills, and the ability to coordinate complex, cross\-functional work in a regulated financial services environment.
Responsibilities:
- Manage the end\-to\-end delivery lifecycle for firmwide AI initiatives, including opportunity intake, prioritization, business case development, planning, execution, dependency management, launch readiness, firmwide roll\-out, and adoption tracking.
- Serve as a hands\-on execution lead who can move between strategic planning and detailed follow\-through, ensuring that AI initiatives maintain momentum while supporting Rockefeller’s fiduciary responsibilities, client trust, and operating discipline.
- Collaborate with Technology, business stakeholders, and other control function partners to ensure AI initiatives are delivered within firm standards, regulatory expectations, and responsible AI governance requirements.
- Maintain an enterprise view of the AI initiative portfolio, including status, milestones, risks, issues, dependencies, decisions, funding needs, vendor engagement, and executive\-level reporting for leadership forums and governance routines.
- Develop clear project plans, roadmaps, RAID logs, decision logs, action trackers, meeting materials, and executive updates that create transparency and drive accountability across multiple stakeholder groups.
- Coordinate AI pilots, proofs of concept, agentic technology initiatives, workflow automation opportunities, and production deployments, ensuring that appropriate success criteria, controls, testing, adoption plans, and support models are defined before launch.
- Support AI governance routines by preparing agendas, materials, readouts, follow\-ups, and decision documentation, and by helping translate technical, operational, and risk considerations into practical business recommendations.
- Partner with product, architecture, engineering, data, and vendor teams to clarify requirements, align delivery plans, manage dependencies, and resolve blockers across internal platforms and third\-party AI solutions.
- Participate in establishing and continuously improving repeatable AI delivery practices, including intake standards, prioritization criteria, implementation playbooks, control checkpoints, measurement frameworks, and operating cadences.
- Drive business readiness and adoption planning for AI capabilities, including stakeholder communications, training coordination, change impact assessment, feedback loops, and post\-launch performance monitoring.
Qualifications:
- Bachelor’s degree in Technology, Business, Finance, or a related discipline; advanced degree preferred
- 7\+ years of experience in technology program, project or product management within financial services or a similarly complex, regulated environment
- Demonstrated experience managing enterprise\-scale AI or Technology programs, including strategic vendor partnerships, platform launches, and cross\-functional delivery
- Strong working knowledge of AI/ML concepts, large language models, agentic AI architectures, and enterprise AI governance frameworks
- Proficiency across the Copilot and Claude toolset — including Copilot Chat, Copilot Studio, Copilot Research, Cowork, Claude Skills, Claude Projects, Excel and Power Point plugins — to optimize research, reporting, project execution, and stakeholder communication.
- Experience with wealth management or financial services technology ecosystems preferred, including familiarity with advisory platforms, data ecosystems, and client\-facing digital products
Skills:
- Strategic thinker with the ability to own a broad AI agenda and drive execution across multiple complex workstreams simultaneously; comfortable setting direction and managing upward.
- Exceptional executive presence and communication skills; proven ability to represent complex program status credibly to C\-suite and board\-level audiences and translate technical concepts into business\-relevant insights.
- Highly collaborative, cross\-functional leader with a demonstrated track record of aligning diverse stakeholders across lines of business, technology teams, and external vendor partners.
- Intellectually curious about AI capabilities and their application to financial services workflows; hands\-on with the platforms and tools they oversee and able to engage substantively with both business and technical counterparts.
- Highly organized with strong technology governance instincts; comfortable maintaining rigor across a large portfolio while keeping delivery teams unblocked and stakeholders informed.
- Entrepreneurial energy combined with disciplined execution; able to build structure in ambiguous environments and drive outcomes from concept through adoption.
Compensation Range:
The anticipated base salary range for this role is $150,000 to $200,000\. Base salary for the role will depend on several factors, including a candidate’s qualifications, skills, competencies, and experience, and may fall outside of the range shown. In addition, this role may be eligible for a discretionary bonus. Rockefeller Capital Management offers a comprehensive benefit package including health coverage, vacation time, paid leave, retirement plan, and more. Visit careers.rockco.com to learn more about additional opportunities and benefits offerings.
Disclosure:
Rockefeller \& Co. LLC, Rockefeller Financial LLC, Rockefeller Trust Company, N.A., The Rockefeller Trust Company (Delaware), Rockefeller Financial Services, Inc. and all other subsidiaries of Rockefeller Capital Management L.P. (individually and collectively, “Rockefeller”) is an equal opportunity employer and does not discriminate on the basis of race, religion, sex, gender, sexual orientation, gender identity or expression, national origin, citizenship, age, military or veteran status, marital or partnership status, caregiver status, legally recognized disability, or any other basis protected by applicable federal, state or local law (“protected characteristics”).
Rockefeller Capital Management participates in the E\-Verify program in certain locations, as required by law.
Salary Context
This $150K-$200K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1841 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 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Rockefeller Capital Management, 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 $218,750 based on 3,817 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000. This role's midpoint ($175K) sits 20% below the category median. Disclosed range: $150K to $200K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Rockefeller Capital Management AI Hiring
Rockefeller Capital Management has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in New York, NY, US. Compensation range: $200K - $200K.
Location Context
AI roles in New York pay a median of $220,000 across 1,045 tracked positions.
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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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