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Infrastructure Engineer-NYC, NY / San Francisco, CA-Onsite Work- F2F Interview- Fulltime

American IT SystemsSan Francisco, CA🇺🇸United StatesPosted 18 Jul 2026

Quick Overview

Work Type
On Site
Level
Mid Senior

Job Description

Infrastructure Engineer
NYC, NY / San Francisco, CA-Onsite Work- F2F Interview
Fulltime
Company Overview
  • provides AI data infrastructure for financial services, focusing on accurate data consolidation and security.
  • The company has strong backing, launched out of stealth, and is supported by experienced founders and top-tier investors.

Role Requirements
  • Product Engineer role requires a strong engineering background, ideally from high-growth startups or top tech companies.
  • Focus on full-stack engineering with a preference for back-end experience; experience in sophisticated product development is crucial.

Candidate Requirements
  • Looking for candidates from top engineering schools, but strong experience can compensate for less prestigious educational backgrounds.
  • Experience in high-growth or established tech firms is preferred; a strong engineering culture is essential.

Compensation and Logistics
  • Positions are based in SF or NY, with onsite work expected, but flexible hours are allowed.

Timeline and Urgency
  • High urgency to hire; open to hiring multiple candidates per role quickly if they meet the criteria.
  • Interview process is swift, involving a 30-minute call, a 50-minute coding interview, and a final onsite project-focused interview.

Pain Points
  • The main bottleneck is the number of engineers, limiting the ability to take on new business opportunities.

Ideal Candidate Profile
  • Motivated by joining an early-stage startup
  • Strong technical skills with a history of significant contributions to high-impact projects.
Seniority

1 - 10 years of experience in cloud or data infrastructure engineering; senior- leaning first NYC hire; 4- 5+ years unless top- 25 CS degree or strong- startup pedigree

Work experience

Experience at a company with a strong, sophisticated engineering bar (e. g. , early team at Stripe, Databricks, Applied Intuition, Decagon, Sierra, Cursor, early Retool, early Scale AI, or equivalent)

One of two archetypes:

Archetype 1: Cloud Infra Engineer deploying across AWS, Google Cloud Platform, Azure.

Archetype 2: Data Infra Engineer building complex data ingestion pipelines.

High slope: demonstrated through strong progression in title/scope changes (e. g. , promoted to senior SWE 1- 2 years early)

ML infrastructure exposure is a strong plus

Regulated industry experience

Education

Degree from a top 25 Computer Science program.

Hard skills

Expertise in at least one major cloud platform (AWS, Google Cloud Platform, Azure) and data pipeline tooling (Databricks, etc. ) with containerization/Kubernetes at scale; data- pipeline tooling is secondary, not the core.

Soft skills

Clear, strong motivation for joining an early- stage startup.

Note: engineers based in NYC should have 4- 5+ years of experience unless they have experience working at a strong startup/have a top 25 cs degree

Traits to avoid

No experience from a company with a strong engineering culture (e. g. , unknown startups, slow- moving companies)

Solely FAANG/big tech experience

About this role

The Role

We're looking for Infrastructure Engineers who will be instrumental in building and securing the backbone of our enterprise-grade AI data platform. You'll design systems that handle large volumes of sensitive financial data under strict security and compliance requirements, including real-time correlation of public data with private, tenant-isolated customer data at scale.

What You'll Do

  • Design and build scalable infrastructure to support our AI-powered knowledge engine processing structured and unstructured financial data

  • Implement security-first architecture for private cloud deployments, ensuring data governance meets financial services requirements

  • Build robust data ingestion pipelines that handle everything from CapIQ feeds [structured data] to internal SharePoint documents [unstructured data]

  • Develop monitoring and alerting systems for our BYOC platform

  • Implement access controls and audit trails to ensure AI interactions are traceable back to primary sources

  • Partner with our AI Research and Product teams to optimize infrastructure for LLM inference and training workloads and building agent infrastructure

  • Establish CI/CD practices and infrastructure-as-code for rapid, reliable deployments orchestrated across multiple cloud providers

What You Have

  • 3-10 years of experience in infrastructure or platform engineering

  • Strong background in cloud platforms (AWS, Google Cloud Platform, or Azure) with expertise in containerization and kubernetes

  • Security-minded approach with experience in data governance, especially in regulated industries

  • Experience with data pipeline technologies and real-time processing frameworks; most any ETL experience will do

  • Knowledge of modern monitoring, logging, and observability tools; we use Datadog

  • Understanding of compliance requirements (SOC, SOX, GDPR, financial services regulations is a plus)

  • Startup mentality with the ability to move fast while building robust, scalable systems

  • Experience with AI platforms like SageMaker or Bedrock

Interview Process:

1. Initial Screen (30 minutes)
30-minute conversation with the hiring manager, Yang, to assess background, fit, and motivations for joining.
2. Coding Interview (15 minutes)
coding interview conducted on Coderpad - focus is on implementation rather than Leetcode-style algorithms questions. AI tools not permitted.
3. Final On-site Interview (5-6 hours)
final, in-person interview that is project-oriented. For more senior candidates, this stage will also include a system design component.

Skills

Swift
AWS
ETL
Azure
Data Pipeline
Databricks
Datadog
GDPR
Google Cloud
Kubernetes
LESS
LLM

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