Software Engineer - Applied AI

About the role

This position is responsible for delivering full-stack artificial intelligence features that directly power retirement products and user experiences. The role owns the implementation of a new retirement mortgage solution and the modernization of the 401(k) user journey through code. You will shape a credit marketplace ecosystem built into a lean fintech environment located in Tribeca, NYC, working closely with cross-functional partners. The work requires translating high-level product intent into robust, production-grade systems and AI components. You will own the entire lifecycle of features from initial design through deployment and iteration. Success in this role requires clear communication to explain complex technical decisions in plain language to non-technical stakeholders. The position demands ownership of both the user-facing experience and the underlying infrastructure that supports it.

Key facts

What you'll do

Design and ship new features for a retirement mortgage experience using general AI systems that translate product requirements into functional software.

Build and fine-tune AI infrastructure components, ensuring models meet the specific compliance and accuracy requirements of the retirement industry.

Develop and improve engineering processes and systems to scale AI products for a mass retirement user base without sacrificing reliability.

Collaborate across product, design, and data teams to reduce wealth inequality by creating a more modern and accessible retirement system.

Implement backend services and APIs using professional full-stack proficiency to support AI-driven features in production.

Write and maintain tests, fix bugs, and optimize performance to ensure a stable and responsive user experience for retirement products.

Allocate dedicated time each week for planning, code review, and debugging to maintain high code quality and team velocity.

Use clear communication to articulate technical trade-offs and decisions, separating strong engineers through the ability to explain complexity simply.

Contribute to the development of agentic systems and embedding AI/ML into user-facing applications to enable feature ownership and autonomy.

Support the creation of a modern 401(k) experience by building the underlying tools and infrastructure required for seamless user interaction.

Work within a lean fintech environment to iterate quickly and deliver measurable impact to users and the business.

Ensure all solutions adhere to regulated data handling standards and incorporate model monitoring practices for long-term stability.

Requirements

The posting states a bachelor's degree requirement, and a degree is required as a baseline for eligibility for this role.

Full-stack proficiency is required to reliably implement AI features using web frameworks, backend systems, and cloud infrastructure.

Professional proficiency in at least one of these languages is required: Kotlin, Swift, Rust, Java, Go, C/C++, Python, Ruby, or TypeScript.

Fintech or early-stage experience is preferred to effectively navigate the retirement mortgage domain and its specific constraints.

Experience with agentic systems and embedding AI/ML in user-facing applications is preferred for taking ownership of features.

The ability to write tests, fix bugs, and improve performance is a standard expectation for all engineers in this role.

Clear communication skills are required, as engineers spend part of every week on planning, code review, and debugging.

Practical notes

The role is based in Tribeca, New York Office, with an in-office schedule.

Typical interview steps

Hiring for engineering roles usually starts with a recruiter screen, followed by one or two technical rounds. Candidates often solve a coding problem, discuss past projects, and answer system design questions. Some loops include a take-home task. Final rounds typically cover team fit and give candidates a chance to ask questions. Interviewers look for how you break down an unfamiliar problem, not just whether you reach the answer. Practicing a few problems aloud and reviewing your own past projects are the best preparation.

Good to know

AI infrastructure roles often combine data pipelines, model training, and production deployment.

General purpose languages such as Python and TypeScript commonly coexist in AI stacks for orchestration and APIs.

FinTech products rely on regulated data handling and model monitoring in production.

Collaboration across product, design, and data roles is typical for retirement and wealth systems.

Modern agentic systems combine prompts, tools, and memory to perform multi-step tasks.

Questions to ask

Good questions to ask the employer in the interview: what does success look like in the first six months, how is the team structured, what is the current biggest challenge, and how are decisions made. Asking about growth paths and the review process is also well received. Employers expect questions, and good ones show preparation.

Career growth

Engineering careers usually progress from individual contributor to senior, staff, and principal levels. Some engineers move into management and lead teams of five to twenty people. Others stay on the technical track. Growth follows demonstrated impact, not tenure alone. A typical engineering ladder has clear levels with defined expectations for scope, quality, and mentorship. Moving up usually requires owning outcomes end to end rather than completing assigned tickets.