Software Engineer

Vestmark•Boston, MA

12d•Onsite

About The Position

Vestmark is building the AI-native future of wealth management technology. We are looking for early-career Software Engineers in Boston who are serious about AI-assisted development, curious about how software engineering is changing, and ready to do real work on systems that matter. You will not be handed a ticket queue. You will be expected to think, build, and grow quickly in an environment where AI fluency is the baseline, not the aspiration. This role is based out of the Boston office in the Seaport District. Vestmark is a leading provider of wealth management solutions and services that enable financial advisors and institutions to efficiently manage and trade their clients' portfolios using a purpose-built SaaS ecosystem. With over $2 trillion in assets and 5+ million accounts, we are a trusted partner to some of the largest and most respected players across the wealth management industry.

Requirements

Solid foundational knowledge of software engineering: data structures, algorithms, system design basics, and how production systems behave under real-world conditions
Ability to build and deliver working solutions in at least one language, with willingness to use whatever the problem requires
Understanding of APIs, basic cloud concepts, and modern software delivery practices
Genuine, hands-on familiarity with AI-assisted development tools: you use them regularly, you have opinions about them, and you are actively developing your practice
Curiosity about agentic systems: how AI can plan, take action, and iterate toward outcomes in multi-step workflows
Awareness of where AI tooling is heading and a desire to stay current as it evolves rapidly
Ability to engage with ambiguous problems: ask good questions, break the problem down, and propose a reasonable approach before jumping to a solution
Comfort working on problems where the full solution isn’t defined upfront
Attention to the business context of the work: understanding why something matters, not just what needs to be built
Demonstrated ability to learn quickly and independently: you don’t wait to be taught everything
Receptive to feedback and able to apply it quickly; the expectation is continuous improvement, and you take that seriously
Evidence of intellectual curiosity beyond coursework: personal projects, open source contributions, self-directed learning in AI or systems engineering
Product Mindset: You engage with the problem before you engage with the solution; you ask why before you ask how, and you want to understand what you're building and whether it's the right thing to build
You form a point of view on what you're building and why it matters; you don't treat requirements as fixed inputs but as the starting point for understanding the problem
B.S. in Computer Science or equivalent work experience; M.S. a plus
0-3 years of professional software engineering experience; strong internship experience or personal project work considered
Demonstrated AI tool fluency matters more here than years of experience

Nice To Haves

Hands-on experience with agentic frameworks, LLM APIs, or workflow orchestration tools, whether through coursework, personal projects, or early professional work
Experience with AWS or equivalent cloud environments, even at a basic level
Background or coursework in financial services, mathematics, or quantitative disciplines
A project, contribution, or body of work you can speak to in depth: something you built, something that broke, and what you learned from both

Responsibilities

Build Agentic Systems: Contribute to the design and delivery of agentic systems: workflows where AI plans, executes, and iterates toward outcomes rather than simply responding to requests
Build and ship production-quality solutions as part of a squad working on real efficiency and automation problems with direct business impact
Participate in the full delivery lifecycle: design discussions, development, testing, deployment, and iteration
Apply AI-Native Development Practices: Use AI-assisted development tools, including code generation, prompt engineering, and agentic frameworks, as a standard part of your daily workflow from day one
Learn and apply the team’s standards for building AI-native systems: state management, observability, non-determinism, and inference cost awareness
Engage with testing and validation approaches specific to adaptive systems where behavior is not fully deterministic
Contribute to Reusable Foundations: Create clean, testable, maintainable solutions that strengthen the team's shared foundation; treat code quality as a direct contribution to everyone who touches this codebase after you
Apply the principle “fix the process, don’t automate the broken one” : understand the problem deeply before proposing a solution
Participate in code reviews as both a reviewer and a recipient; treat both as learning opportunities
Grow & Contribute to the Team: Engage actively in design reviews and architecture discussions, asking questions and developing your point of view over time
Take feedback seriously and apply it quickly; growth velocity matters here as much as current skill level
Contribute to the growth and development of our core values: We Before Me, Positive Energy, Knowledge Explorer, and Own It