This role pays less than
63%
of similar roles. Most pay
$150,187–$197,500
— the shaded band above.
At the midpoint, this role pays about
$164k
versus about
$174k
for comparable roles.
Based on 240 similar postings.
Employer
About T. Rowe Price
T. Rowe Price is an asset management firm focused on delivering global investment management excellence and retirement services
T. Rowe Price currently has
29 open roles
on FindRole.
Listed pay typically runs
$121,000–$206,000
across 29 roles with salary data.
Sr. Software Engineer- Multi-Asset Technology joins the Multi-Asset Front Office Technology team to design, build, and enhance a suite of next-generation, cloud-native applications supporting the Investment Division. This role involves independently solving complex technical challenges, influencing architecture decisions, and managing the full product lifecycle from development through deployment. The engineer will partner with portfolio managers, quantitative researchers, and traders to translate business needs into maintainable solutions for multi-asset investment workflows. Key technologies include Python, Java, JavaScript, React, Node.js, Spring Boot, and Pandas. The role requires expertise in cloud technologies, containerization, and database systems like SQL Server, Oracle, MongoDB, PostgreSQL, or Redshift. Additionally, the candidate will build AI-enabled applications using LLMs and modern frameworks, utilizing tools such as Cucumber and Cypress for automated testing while ensuring high reliability for complex financial data and investment strategies.
What does a Software Engineer earn in Maryland?
Median $179000 from 38 postings across 6 companies.
Design, build, and maintain scalable, cloud-native applications to support multi-asset investment workflows.
Translate complex business requirements from portfolio managers and traders into practical technical solutions.
Lead technical design decisions, architecture discussions, and code reviews to establish engineering standards.
Implement modern software practices including agile delivery, CI/CD, automated testing, and secure coding.
Troubleshoot complex technical issues and implement durable fixes to improve system reliability and performance.
Build AI-enabled applications and intelligent workflows using large language models and modern AI frameworks.
Mentor other engineers by sharing knowledge and promoting best practices within the team.
What we're looking for
BS or MS degree in Computer Science, Mathematics, Engineering, Physics, or a related field with meaningful mathematical and computing content, or equivalent practical experience.
Typically 5+ years of professional software engineering experience including designing, building, testing, and supporting production applications.
Strong proficiency in one or more programming languages such as Python, Java, or JavaScript to write clean, maintainable, and testable code.
Experience with modern frameworks and tools like Pandas, React, Node.js, and Spring Boot.
Experience designing and deploying solutions using cloud technologies, containerization, serverless architectures, and related DevOps practices.
Quality-focused approach including experience with automated testing tools such as TDD, BDD, Cucumber, and Cypress.
Experience with one or more database technologies such as SQL Server, Oracle, MongoDB, PostgreSQL, or Redshift.
Hands-on experience building AI-enabled applications or agentic systems using modern LLMs and AI development frameworks.
Experience with web-based development and visualization for complex datasets (preferred).
Knowledge of mathematical concepts like statistics, time-series analysis, asset pricing theory, or optimization algorithms (preferred).
Strong understanding of algorithms, data structures, design patterns, and scalable application design (preferred).
Understanding of Multi-Asset investment strategies, financial instruments, derivatives, and capital markets (preferred).
Experience partnering with front office clients like Portfolio Managers, Quants, and Traders (preferred).
Experience mentoring engineers or improving team practices (preferred).
Practical experience with modern AI engineering patterns such as agent orchestration and retrieval-based systems (preferred).
Experience taking AI-enabled systems from prototypes to production including testing and monitoring (preferred).