Infrastructure Engineers

• 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., 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, 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.

1. 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.

• 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., * 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), * 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

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.