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At T. Rowe Price, we identify and actively invest in opportunities to help people thrive in an evolving world. As a premier global asset management organization with more than 85 years of experience, we provide investment solutions and a broad range of equity, fixed income, and multi-asset capabilities to individuals, advisors, institutions, and retirement plan sponsors. We take an active, independent approach to investing, offering our dynamic perspective and meaningful partnership so our clients can feel more confident.
We believe doing the right thing for our clients and our associates is good business. With a career at the firm, you can expect opportunities to create real impact at work and in your community. You’ll enjoy resources to support your career path, as well as compensation, benefits, and flexibility to enrich your life. Here, you’ll find a collaborative culture that respects and values differences and colleagues who share a spirit of generosity.
Join us for the opportunity to grow and make a difference in ways that matter to you.
Role Summary
The Investment Strategist, Artificial Intelligence, serves as a critical link between T. Rowe Price’s investment organization and clients, helping articulate how artificial intelligence and other innovative tools are thoughtfully and responsibly used across the investment process.
Working closely with the Head of Investment AI Solutions and investment professionals across the firm, the Strategist will develop a deep understanding of how AI is being applied to research, idea generation, portfolio construction, risk management, and investment decision making—and translate the investment platform’s perspectives into clear, compelling messages for clients, prospects, consultants, and internal stakeholders.
The Strategist will serve as a leading investment-facing subject matter expert on AI and innovation, explaining where technology can enhance the investment process, where human judgment remains essential, and how T. Rowe Price’s approach supports its active investment philosophy. The role will also represent the investment platform’s perspectives on the implications of AI for markets and investing, contribute to thought leadership, and bring client and market insights back into the organization.
This is a highly visible, client-facing role with approximately 20–40% travel in the U.S. and globally with engagements ranging from one-on-one discussions to presentations for audiences of 50 or more.
Responsibilities
- Represent our investment approach to AI. Serve as a leading investment-facing resource on AI and innovation with clients, prospects, consultants, distribution partners, and at industry events.
- Build and maintain deep platform knowledge. Partner closely with Investment AI Solutions, investment professionals, technology teams, and other subject matter experts to understand how AI and emerging technologies are being applied across the investment process.
- Translate complexity into compelling investment narratives. Develop presentations, thought leadership and standardized messaging that make complex AI concepts practical and relevant to investment audiences.
- Support commercial outcomes. Partner with sales, relationship management, Portfolio Specialists, The Lab @ T. Rowe Price, marketing, communications, and RFP teams to support client acquisition and retention and strengthen understanding of T. Rowe Price’s investment capabilities.
- Represent investment perspectives on AI as an investment theme. Understand and communicate the investment platform’s views on the implications of AI for markets, industries, and investment opportunities, connecting those perspectives to relevant T. Rowe Price capabilities.
- Bring the outside in. Monitor developments in AI and capture feedback from clients, consultants, and distribution partners to help inform messaging, education, thought leadership, and client engagement priorities.
Qualifications
Required:
- 7–10+ years of relevant investment management experience, such as investment research, consulting, client service, investment communications, or a related investment role.
- Broad knowledge of financial markets and the investment process, combined with demonstrated expertise or strong experience in artificial intelligence and emerging technologies.
- Ability to understand and explain how AI can enhance investment research and decision making, including its capabilities and limitations.
- Strong client-facing, presentation, communication, and storytelling skills, with demonstrated ability to translate complex investment and technology concepts for diverse audiences.
- Strong relationship and collaboration skills across investment, technology, distribution, marketing, and client-facing teams.
- Intellectual curiosity, sound judgment, and the ability to operate effectively in a fast-paced and evolving environment.
- Willingness to travel approximately 20–40% in the U.S. and globally.
Preferred:
- MBA, CFA, or equivalent investment experience.
- Experience with AI, data science, investment technology, or digital innovation within investment management.
- Familiarity with generative AI, agentic AI, and emerging AI capabilities relevant to investment organizations.
- Ability to work effectively in a collaborative, fast-paced, and evolving environment.
- Highly motivated self-starter with strong leadership capabilities, intellectual curiosity, personal integrity, and a passion for advancing both investment excellence and client outcomes.
FINRA Requirements
FINRA licenses are not required and will not be supported for this role.
Work Flexibility
This role is eligible for hybrid work, with up to one day per week from home.
Base Salary Ranges
Please review the job posting for the location of this specific opportunity.
$200,000.00-$300,000.00 for the location of: Maryland, Colorado, Washington and remote workers
$200,000.00-$300,000.00 for the location of: Washington, D.C.
$200,000.00-$300,000.00 for the location of: New York, California Placement within the range provided above is based on the individual’s relevant experience and skills for the role. Base salary is only one component of our total compensation package. Employees may be eligible for a discretionary bonus, which is determined upon company and individual performance.
Commitment to Diversity, Equity, and Inclusion
At T. Rowe Price, our associates are our greatest asset. We thrive because our company culture is built on inclusion and because we sustain a work environment where associates can bring their best selves to work every day. The backgrounds, talents, and experiences of our global associates allow us to embrace new ideas and perspectives that move our business priorities forward and enable us to deliver strong client outcomes. Here, you can expect equal opportunity and fair and consistent treatment for all.
Benefits
We value your goals and needs, at work and in life. As an associate, you’ll be supported with resources, benefits, and work-life balance so you can thrive in ways that matter to you.
Featured employee benefits to enrich your life:
- Competitive compensation
- Annual bonus eligibility
- A generous retirement plan
- Hybrid work schedule
- Health and wellness benefits, including online therapy
- Paid time off for vacation, illness, medical appointments, and volunteering days
- Family care resources, including fertility and adoption benefits
Learn more about our benefits.
T. Rowe Price is an equal opportunity employer and values diversity of thought, gender, and race. We believe our continued success depends upon the equal treatment of all associates and applicants for employment without discrimination on the basis of race, religion, creed, color, national origin, sex, gender, age, mental or physical disability, marital status, sexual orientation, gender identity or expression, citizenship status, military or veteran status, pregnancy, or any other classification protected by country, federal, state, or local law.
Salary Context
This $200K-$300K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At T. Rowe Price, 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 in Demand for This Role
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 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($250K) sits 18% above the category median. Disclosed range: $200K to $300K.
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.
T. Rowe Price AI Hiring
T. Rowe Price has 7 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Positions span New York, NY, Baltimore, MD, US, Baltimore, MD. Compensation range: $300K - $300K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 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 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).
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 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
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