Senior Applied AI Research Engineer

Monstro•New York, NY

4d•$194,000 - $235,000

About The Position

Monstro is seeking a Senior Applied AI Research Engineer to join their AI team. This role involves architecting and implementing AI systems for financial analysis, building tools for domain experts, and establishing engineering standards for responsible AI development in a regulated industry. The position will influence the design of agents, validation of AI output, scaling of modeling infrastructure, and considerations for auditability and compliance. The intelligence layer is central to the product at Monstro.

Requirements

5-8+ years of engineering experience, with a proven track record of leading complex systems from design to production.
Hands-on experience with AI/ML systems, including agent architectures, LLM integration, model evaluation, or production AI pipelines.
Expertise in Python, including async services, type-safe code, and scalable systems.
Experience building AI systems in regulated industries (financial services, healthcare, legal) or with compliance, auditability, or explainability requirements.
Strong distributed systems fundamentals: event-driven architectures, message queues, job state machines, and worker patterns.
Proficiency in relational databases and data modeling.
Full-stack capabilities, including building product-quality React/TypeScript UIs.
Strong opinions on testing, evaluating, and monitoring AI systems in production.

Nice To Haves

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.

Responsibilities

Architect and build AI agent systems, including multi-agent workflows, orchestration layers, and production-ready infrastructure.
Design modeling and reasoning systems that meet financial services' requirements for correctness and explainability, ensuring traceability and defensibility of recommendations.
Build internal tooling platforms (Python/FastAPI) for authoring, validating, and deploying AI-powered analysis at scale.
Own distributed backend infrastructure, including event-driven workers, job pipelines, and multi-stage processing systems for reliable AI output delivery under load.
Establish patterns for AI output validation and quality assurance, such as automated evaluation pipelines, hallucination detection, and compliance guardrails.
Drive technical decisions across teams, including schema design, API contracts, system boundaries, and service architecture.