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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
This is a rare opportunity to sit at the frontier of investment and artificial intelligence, building systems that redefine what’s possible in active management. You will help pioneer a new generation of AI\-powered investment capabilities that expand human insight, accelerate alpha generation, and shape the future of how T. Rowe Price delivers value to clients globally.
The Investment AI Solutions Analyst plays a pivotal role in realizing that future. This role combines deep investment knowledge with leading technical expertise in AI systems design to create, deploy, and operationalize agentic AI. These agents will enhance every facet of the investment process, from research discovery and portfolio construction to risk analysis and client engagement. Working alongside experienced analysts and portfolio managers, this role will help deliver innovative AI insights that inform better investment decisions. This is an opportunity to be part of a team deeply engaged in pioneering AI research and modern quantitative approaches, collaborating directly with our fundamental research platform on initiatives that help shape the future of the firm’s investment strategies.
Guiding teams of investors, AI engineers, technologists, and architects, you will enhance the capabilities of investors and shape the next era of investment management where autonomous systems reason, plan, and act in collaboration with human investors. These systems will not only drive superior investment outcomes but also uncover new opportunities and redefine how institutional investors generate insight at scale.
Responsibilities
Build Strategic Relationships with Stakeholders:
- Develop and maintain strong, consultative relationships with fundamental analysts and portfolio managers (PMs). Understand each stakeholder's investment framework, objectives, and decision\-making approach.
- Serve as a highly regarded subject matter expert in AI. Identify opportunities where AI can add meaningful value to fundamental investors.
- Define the product roadmap, assess feasibility, and establish accountability in close partnership with stakeholders.
Architect the Next Generation of Investment Intelligence:
- Design and deliver agentic architectures that integrate LLMs, quantitative models, enterprise data pipelines, and real\-time reasoning systems.
- Deploy and drive adoption of agents that understand financial context, retrieve relevant knowledge, and generate research\-grade outputs that enhance the work of investors.
- Implement robust guardrails, compliance checks, and observability frameworks to ensure agents operate reliably in production investment environments.
- Continuously evaluate and integrate emerging frameworks in multi\-agent coordination, retrieval\-augmented generation (RAG), and prompt design.
Fuse AI and Research:
- Partner with fundamental and quantitative investors to identify high\-value opportunities for AI\-enabled automation and augmentation.
- Translate complex investment problems into structured AI workflows that generate measurable alpha and enhance decision velocity.
- Push relevant research proactively to analysts and PMs, supplementing their ongoing decision\-making process.
- Present research and recommendations confidently in meetings and formal presentations to the investment team.
Collaborate, Lead, and Transform:
- Work across Investment, Data Science, and Technology teams to deliver AI\-native capabilities that modernize T. Rowe Price’s investment platform.
- Lead workflow transformations that translate complex processes into intelligent agentic solutions.
- Continuously develop investment workflow prototypes to test the feasibility of fully autonomous, self\-improving systems.
- Help institutionalize best practices for prompt engineering, orchestration, and responsible AI across the investment organization.
Success Measures
- Strength and breadth of relationships with key stakeholders (Analysts, PMs, Investments leadership).
- Measurable productivity gains from agentic workflows through increased awareness and utilization by fundamental investors.
- Measurable improvement to investment results and incremental revenue generation.
- Ideation, design, and execution of next\-generation investment management capabilities.
Qualifications
Required:
- B.S. in a quantitative discipline (Computer Science, Applied Mathematics, Statistics, or related field).
- Expert\-level proficiency in programming languages (R or Python) and experience applying these to agentic AI systems (e.g., LangChain, AutoGen, CrewAI, Semantic Kernel, or LangGraph).
- Exceptional skills in prompt engineering, assessing use cases for agentic feasibility, and converting complex workflows into logical, step\-by\-step tasks.
- Deep expertise in data modeling, processing, integration, and analytics, with demonstrable proficiency in enterprise data platforms (e.g., Snowflake, Databricks, BigQuery).
- Strong track record of innovation and problem\-solving to find creative technical solutions to new and unforeseen business problems.
- Ability to travel to support deployments across the global organization.
- Strong communication skills, with the ability to synthesize complex AI and investment concepts for diverse audiences.
- 5\+ years’ experience with financial modeling, portfolio construction, trading, or investment management workflows.
Preferred:
- M.S. or Ph.D. in a quantitative discipline.
- Leadership experience driving technology and investment teams.
- 3\+ years developing agentic AI systems (e.g., LangChain, AutoGen, CrewAI, Semantic Kernel, or LangGraph).
- 10\+ years’ experience with financial modeling, portfolio construction, trading, or investment management workflows.
- Experience with advanced retrieval\-augmented generation and multi\-agent systems.
- Certifications such as:
+ Databricks Certified Generative AI Engineer Associate
+ SnowPro® Specialty: Gen AI
+ AWS Certified Machine Learning Engineer – Associate
+ AWS Certified Machine Learning – Specialty
- CFA charterholder or active candidate.
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.
$133,000\.00\-$200,000\.00 for the location of: Maryland, Colorado, Washington and remote workers
$133,000\.00\-$200,000\.00 for the location of: Washington, D.C.
$133,000\.00\-$200,000\.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 $133K-$200K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($166K) sits 23% below the category median. Disclosed range: $133K to $200K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
T. Rowe Price AI Hiring
T. Rowe Price has 2 open AI roles right now. They're hiring across AI Software Engineer, AI/ML Engineer. Positions span New York, NY, US, Baltimore, MD, US. Compensation range: $200K - $206K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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