Member of Technical Staff, Platform Engineer

Member of Technical Staff, Platform Engineer

Location

New York, NY

Work Arrangement: On-site, five days per week

Office Location: Flatiron, New York City

Company Stage

Early-stage, venture-backed company

Compensation

Base Salary: $200,000–$300,000

Equity: Equity offered

Bonus: Bonus compensation offered

Visa

Open to eligible visa transfers, including OPT and H-1B transfers. New visa sponsorship is not available.

Company Description

We are building an AI-native wealth management platform designed to transform how financial advisors serve their clients. The platform combines AI-powered agents, client context, and workflow automation to help advisors spend less time on repetitive administrative work and more time delivering personalized advice.

Unlike traditional wealth management systems built around periodic reviews and static financial plans, the platform maintains an evolving understanding of each client's circumstances, remembers important details over time, detects meaningful life changes, and helps advisors respond proactively.

The business was built through the acquisition of established wealth management firms, providing access to real advisors, real clients, and substantial assets under management from the outset. This creates an opportunity to build production systems that operate in a real financial environment rather than relying solely on prototypes or experimental data.

The engineering team is small, technically strong, and focused on shipping high-impact products. Platform infrastructure is central to the business: AI agents rely on the underlying systems to access information, execute workflows, and support client-facing operations reliably and securely.

The company emphasizes ownership, engineering excellence, practical AI implementation, and close collaboration. Engineers are expected to build foundational systems that enable the entire organization to move quickly without compromising reliability, performance, or cost.

What You Will Do

Own core platform infrastructure: Build and maintain the infrastructure, backend systems, and developer tooling that power the company's AI-driven wealth management platform.

Build reliable backend systems: Design and implement production-grade services, APIs, data pipelines, and supporting infrastructure for a growing AI-native application.

Create safe execution environments for AI agents: Develop the infrastructure and execution boundaries that allow agents to operate reliably across client workflows and interact with systems of record.

Design for continuous, large-scale agent activity: Build systems that support agents operating across clients and workflows continuously, with strong reliability, performance, and cost controls.

Improve platform reliability: Establish and strengthen the monitoring, testing, deployment, and operational practices needed to support systems used in a regulated, high-stakes financial environment.

Optimize performance and infrastructure costs: Identify bottlenecks, improve system efficiency, and make architectural decisions that balance reliability, latency, scalability, and cost.

Build developer experience tooling: Create tools, workflows, and infrastructure abstractions that help a small engineering team develop, test, deploy, and operate software efficiently.

Own cloud infrastructure and deployment systems: Work with AWS, Docker, Kubernetes, Terraform, and CI/CD pipelines to build and operate production infrastructure.

Develop data infrastructure: Build and maintain data pipelines, database systems, and backend services that support reliable access to client information and platform workflows.

Enable rapid product development: Remove infrastructure and tooling bottlenecks so engineers can ship AI-powered capabilities without being slowed down by platform limitations.

Support agent reliability: Build the systems, controls, and operational foundations required for AI agents to interact with sensitive client data and execute workflows predictably.

Scale the platform through acquisitions: Strengthen the architecture and infrastructure as the business integrates additional wealth management firms and expands across multiple acquired businesses.

Make foundational architectural decisions: Take ownership of technical choices that affect the reliability, security, performance, cost, and scalability of the platform.

Work across the engineering stack: Collaborate with application engineers and AI-focused engineers to ensure the platform supports the requirements of production agentic systems.

Ideal Candidate Background

Required Experience

5+ years of professional experience in backend engineering, platform engineering, infrastructure engineering, or a closely related field.

Strong experience building and operating production backend systems or infrastructure.

Experience designing reliable services, APIs, data systems, or distributed infrastructure.

Experience deploying and maintaining applications in cloud environments.

Ability to take ownership of infrastructure and backend systems from design through production operation.

Strong understanding of reliability, performance, scalability, and operational trade-offs.

Experience improving developer productivity through tooling, automation, CI/CD, or infrastructure abstractions.

Ability to diagnose complex technical issues and improve systems through first-principles problem-solving.

Comfort working in a small, fast-moving engineering team where infrastructure decisions directly affect the entire product.

Interest in building the foundational systems required to run AI agents in production.

Willingness to work on-site in Flatiron, New York City, five days per week.

Technical Skills

Python: Experience building backend services, APIs, automation, or infrastructure-related tooling.

TypeScript: Experience building backend services, platform components, or application infrastructure using TypeScript.

PostgreSQL: Experience with relational database design, query performance, data modeling, and production database operations.

AWS: Experience designing, deploying, and operating cloud infrastructure.

Docker: Familiarity with containerization and reproducible application environments.

Kubernetes: Experience deploying, scaling, and managing containerized workloads.

Terraform: Experience managing infrastructure as code and maintaining repeatable infrastructure configurations.

CI/CD: Experience building and improving continuous integration and deployment pipelines.

Data pipelines: Experience building reliable systems for data ingestion, transformation, synchronization, or processing.

REST APIs: Experience designing and operating backend APIs used by applications, services, or automated systems.

Distributed systems: Understanding of service reliability, failure modes, concurrency, and system performance.

AI agents and LLMs: Familiarity with agent frameworks, LLM-powered applications, or infrastructure supporting AI workloads is highly relevant.

Production operations: Experience with monitoring, logging, debugging, deployment, incident response, and reliability improvements.

Platform tooling: Ability to build tools and abstractions that improve the development and operational experience for other engineers.

Preferred Background

Experience building infrastructure for AI-powered products or agentic systems.

Experience designing safe execution environments, sandboxing, or permission boundaries for automated systems.

Experience operating systems that execute workflows continuously or at high volume.

Experience in fintech, wealth management, financial services, or another regulated environment.

Experience working with sensitive customer data and systems that require strong reliability and access controls.

Experience scaling infrastructure in a small, high-growth startup.

Experience building systems that prioritize reliability, latency, and cost as core product requirements.

Experience integrating multiple data sources or backend systems into a unified platform.

Experience supporting multiple applications, services, or customer environments on a shared platform.

Experience with infrastructure consolidation, platform standardization, or integrating systems following acquisitions.

Experience enabling AI agents to interact with databases, APIs, and systems of record.