Monstro
Staff Applied AI Software Engineer
New York City, Hybrid · Staff+
Sponsorship not specified$224k-$264kDetected 14 days ago
TypeScriptPythonReactFastAPIDistributed SystemsFull-Stack DevelopmentMachine LearningLLMsAI OrchestrationFinancial AnalysisLeadership
About the role
- This isn't a role where AI is a thin wrapper - the intelligence layer is the product.
Responsibilities
- Architect and build AI agent systems - including multi-agent workflows, orchestration layers, and the infrastructure that makes them reliable and auditable in production
- Design modeling and reasoning systems that meet the correctness and explainability requirements of financial services - where every recommendation needs to be traceable and defensible
- Build internal tooling platforms (Python/FastAPI) that enable domain experts and engineers to author, validate, and deploy AI-powered analysis at scale
- Own distributed backend infrastructure: event-driven workers, job pipelines, and multi-stage processing systems that deliver AI outputs reliably under load
- Drive technical decisions across teams: schema design, API contracts, system boundaries, and service architecture
- 8+ years of engineering experience, with a track record of leading complex systems from design to production
- If you're excited to contribute to a high-bar team building something meaningful, we'd love to hear from you.
- Ready to Build With Us?
- For clients, that means clearer decisions, proactive support when life changes, and a better view of their financial lives.
- Every recommendation reflects the institution's own views, policies, and permissions.
Requirements
- Hands-on experience with AI/ML systems - agent architectures, LLM integration, model evaluation, or AI pipelines in production
- Python expertise - async services, type-safe code, and systems that scale
Nice to have
- Experience designing multi-agent systems, agentic workflows, or AI orchestration layers
- Background in quantitative finance, financial planning, or wealth management technology
- Experience leading technical teams or acting as a force multiplier across engineering
- Experienced Team: Join a team with leadership that has a track record of scaling companies from early stage to major exits.
- Principles-Driven Culture: Work in a culture that values speed, ownership, and impact-what most companies achieve in 90 days, we do in 45.
- Base Compensation Range (New York City): $224,000 - $264,000
- A Note on Interviewing
- We sometimes use AI note-takers to help us transcribe interview notes, so we can be more present in your interview.
Skills
- Relational database fluency and an instinct for data modeling
- Opinionated about how AI systems should be tested, evaluated, and monitored in production
Compensation
- Competitive salary, equity, and robust benefits package, including paid health, vision, dental, and disability coverage.
- Final compensation will depend on a variety of factors, including experience, skills, internal leveling, and market conditions, and will be offered within the stated range in accordance with applicable pay transparency laws.
- $224,000 - $264,000
Benefits
- bringing together their full financial picture, identifying what matters, and delivering personalized guidance across investments, tax, retirement, insurance, legal, and more.
- Experience building AI systems in regulated industries (financial services, healthcare, legal) or with compliance, auditability, or explainability requirements
Company info
- Work in a culture that values speed, ownership, and impact-what most companies achieve in 90 days, we do in 45.
- What We're Looking For
Visa & Work Authorization
- If you'd like to opt out of us using automatic transcribers, please note this in the free text field in your application, otherwise we'll take your application as confirmation that you're happy for us to use note-takers (whether added to vi
Apply directly at Monstro →Create a free account for alerts like thisView Monstro immigration profile
This listing is sourced directly from Monstro's careers page and normalized into a canonical job model.