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About This Role
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
Help shape the future of investment research at T. Rowe Price.
We are seeking an Investments AI Enablement Lead to accelerate how investment professionals use AI to enhance research, decision\-making, and portfolio insight. In this role, you will help investors apply advanced AI capabilities to real\-world research and portfolio challenges across Equity, Fixed Income, Multi\-Asset, and other investment teams
This is a highly visible opportunity to sit at the intersection of investments, technology, and innovation. You will translate cutting\-edge internal and external AI capabilities into practical, high\-value workflows that improve the quality, speed, and depth of investment insight. From research discovery to agentic workflows, you will help ensure AI becomes an everyday advantage for our investors.
Working directly with portfolio managers, analysts, directors of research, and other investment professionals, you will guide how AI is integrated into the investment process. You will provide hands\-on coaching, improve prompting and workflow design, and help teams choose the right tools for the right tasks. In doing so, you will strengthen the reliability, safety, and impact of AI\-assisted investment work across the firm.
You will also partner closely with AI engineers, researchers, and product managers to influence the evolution of emerging AI and agentic capabilities—ensuring they deliver measurable value from day one.
Responsibilities
Accelerate AI adoption across Investments:
- Stay current on emerging AI technologies, tools, and industry best practices
- Design and deliver structured training through office hours, one\-on\-one coaching, and small\-group sessions tailored to investment workflows
- Help investment professionals become productive quickly by providing practical guidance, best practices, and hands\-on support across AI platforms
- Partner with colleagues in London and APAC to provide follow\-the\-sun support and promote globally consistent best practices
- Triage and prioritize incoming requests in partnership with Global Desktop Support and platform teams, while leading strategic AI workflow implementation for investors
Improve output quality and create scalable assets:
- Review AI\-assisted workflows with users and recommend improvements to prompting workflow design, tool selection, and methods to improve quality and accuracy
- Develop reusable assets such as workflow templates, prompt libraries, playbooks and best practices for common investment research and portfolio workflows
- Refine workflows for high\-value use cases such as earnings call analysis, meeting synthesis, research summarization, and investment idea generation
- Create educational materials to support broader training programs and adoption initiatives
Guide agentic workflows and influence product evolution:
- Synthesize user feedback to identify the highest\-value vendor capabilities and agentic opportunities, helping inform roadmap priorities
- Advise investors on when to use conversational AI tools versus structured agentic workflows, including task design, guardrails, and handoffs
- Partner with AI engineers, product teams, and platform specialists to support onboarding of new agents and improve capabilities through iterative feedback
- Help teams adopt new features and ensure advanced AI capabilities translate into measurable investment value
Promote responsible use and measurable outcomes:
- Reinforce safety, compliance, and data stewardship expectations in all training and workflow consultations
- Help users apply secure workflows for sensitive research content and data
- Measure outcomes such as time saved, user sentiment, and workflow improvements to evaluate impact and inform future training and product enhancements
- Deliver regular updates to leadership on adoption, usage patterns, and opportunities for improvement across investment teams
- Prepare and deliver high\-quality presentations, reports, and stakeholder communications
- Strong and trusted relationships across the Investment organization
- Increased usage, confidence, and satisfaction with AI capabilities among investment professionals
- Scalable repositories of educational content, best practices, and workflow assets
- Demonstrated improvement in research efficiency, insight generation, and investment outcomes driven by AI adoption
Qualifications
Required:
- Bachelor’s degree (or equivalent practical experience) in Investments, Computer Science, Engineering, Data Science, Economics, Business, or rother analytical discipline.
- Experience in product management, technology strategy, or investor enablement within an investment environment.
- 5\+ years’ experience working with investment professionals (asset management, equity/credit research, or adjacent financial services).
- Clear passion for AI, demonstrated through meaningful hands\-on use of multiple AI platforms across professional workflows
- Experience in AI enablement, product management, consulting, workflow transformation, technology strategy, investment research. or a related role supporting investment or financial services professionals. Experience working closely with investment professionals or delivering technology solutions into investment, financial services, fintech or adjacent knowledge\-work environments.
- Strong understanding of modern AI technologies, including LLMs, generative AI, prompting best practices, and practical applications in investment management
- Exceptional communication and interpersonal skills, with the ability to influence stakeholders across levels and functions
- Proven ability to develop and lead programs in partnership with investment teams to drive meaningful outcomes
Preferred:
- Master’s degree or MBA in Investments, a technical field, or another quantitative discipline
- Familiarity with emerging AI capabilities including agentic workflows, orchestration, retrieval\-augmented generation (RAG), and evolving foundation model capabilities.
- CFA charter holder or active CFA candidate is a plus.
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.
Why this role matters
This role is central to one of the most important transformations underway at T. Rowe Price: redefining how investment research is conducted and how insight is generated through AI. You will help investors work more effectively, influence the future of AI\-enabled investment workflows, and play a direct role in expanding the impact of intelligent systems in active management.
If you are excited by the opportunity to combine investment expertise, AI innovation, and hands\-on user enablement in a highly strategic role, we’d love to hear from you.
Base Salary Ranges
Please review the job posting for the location of this specific opportunity.
$111,000\.00\-$166,000\.00 for the location of: Maryland, Colorado, Washington and remote workers
$111,000\.00\-$166,000\.00 for the location of: Washington, D.C.
$111,000\.00\-$166,000\.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.
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Salary Context
This $111K-$166K 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 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 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. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($138K) sits 37% below the category median. Disclosed range: $111K to $166K.
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.
T. Rowe Price AI Hiring
T. Rowe Price has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Baltimore, MD, US. Compensation range: $166K - $166K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 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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