Infrastructure Engineer

Who is Recruiting from Scratch:

Recruiting from Scratch is a premier talent firm that focuses on placing the best product managers, software, and hardware talent at innovative companies. Our team is 100% remote and we work with teams across the United States to help them hire.

Infrastructure Engineer
Location

San Francisco, CA or New York, NY

On-site 5 days per week. Flexible working hours.
Company Stage of Funding

Series A - $50M Raised
Office Type

On-site - 5 days per week
Salary

$150,000 - $300,000 Base

Flexibility available for exceptional candidates.
Equity

Competitive Equity
Visa

Open to Visa Transfers, including OPT and H-1B transfers. Visa sponsorship is also available.
Experience

3+ years of infrastructure or platform engineering experience.

For NYC-based candidates, 4-5+ years is preferred unless they have strong startup experience or a top-tier CS background.
Employment Type

Full-time
Hiring Count

5 candidates

Company Description

This is a Series A AI infrastructure company building enterprise-grade data infrastructure for financial services.

The company helps financial organizations securely consolidate, process, and understand large volumes of structured and unstructured data. Its platform is designed to handle sensitive financial information while maintaining strong security, governance, tenant isolation, and traceability.

The engineering team is small and highly technical, with the opportunity to work alongside experienced founders and engineers who have helped scale major technology companies. The company is growing rapidly due to strong customer demand, creating an opportunity for infrastructure engineers to have significant ownership over the technical foundation of the platform.

This is an early-stage environment where engineers are expected to move quickly while maintaining high engineering standards. The role sits at the intersection of cloud infrastructure, data infrastructure, security, and AI systems.

What You Will Do
1. Build & Scale Cloud Infrastructure

• Design and build scalable infrastructure supporting an enterprise-grade AI data platform.
• Build and operate infrastructure across AWS, GCP, and/or Azure.
• Develop containerized and Kubernetes-based systems capable of handling large-scale workloads.
• Build infrastructure that supports both cloud and private-cloud deployments.
• Establish scalable architecture capable of supporting rapidly growing customer and data volumes.
• Contribute to the technical foundation of the platform as the company scales.

2. Build Data Infrastructure & Ingestion Systems

• Build robust data ingestion pipelines for structured and unstructured financial data.
• Work with large-scale data sources and real-time processing systems.
• Build systems that ingest and process information from external data providers, enterprise documents, and customer data sources.
• Design reliable pipelines that maintain data quality, availability, and security.
• Partner with product and AI teams to ensure infrastructure can support increasingly sophisticated data workloads.

3. Build Security, Reliability & Observability

• Implement security-first infrastructure for sensitive financial and enterprise data.
• Build data governance, access controls, and audit trails across the platform.
• Develop monitoring, logging, alerting, and observability systems for production infrastructure.
• Help establish infrastructure practices that support compliance requirements across regulated environments.
• Improve reliability and operational visibility using modern observability tooling.
• Build systems that make AI interactions and data workflows traceable back to their underlying sources.

4. Support AI & Machine Learning Infrastructure

• Partner with AI Research and Product teams to optimize infrastructure for LLM inference and training workloads.
• Build and support infrastructure for AI agents and AI-powered data workflows.
• Work with technologies such as SageMaker and Bedrock to support machine learning workloads.
• Design infrastructure capable of supporting increasingly complex AI applications.
• Establish CI/CD and infrastructure-as-code practices for rapid, reliable deployments across cloud environments.

Ideal Candidate Background
Experience Requirements

• 3+ years of infrastructure, platform, cloud, or data infrastructure engineering experience.
• Strong experience working at a company with a sophisticated engineering culture.
• Experience in one of two core archetypes:
  • Cloud Infrastructure Engineer: Building and deploying infrastructure across AWS, GCP, Azure, or similar environments.
  • Data Infrastructure Engineer: Building complex data ingestion, processing, or pipeline systems.
• Demonstrated progression in technical scope, ownership, or seniority.
• Strong candidates may come from high-growth startups or established technology companies with strong engineering standards.
• For NYC-based candidates, 4-5+ years of experience is preferred unless offset by strong startup pedigree or a top-tier CS background.
• Candidates must have at least 1 year of full-time professional experience after graduation .

Technical Requirements

• Strong experience with at least one major cloud platform: AWS, GCP, or Azure .
• Strong Kubernetes and containerization experience.
• Experience building and operating production infrastructure.
• Experience with data pipeline or ETL technologies.
• Experience with infrastructure-as-code and automated deployment practices.
• Familiarity with modern monitoring, logging, and observability tools such as Datadog.
• Strong understanding of distributed systems, reliability, scalability, and production operations.
• Ability to work across infrastructure and data systems rather than focusing exclusively on one tool.

Infrastructure, Data & Security Requirements

• Experience designing scalable cloud infrastructure.
• Experience building robust data ingestion and processing pipelines.
• Strong security mindset when working with sensitive or customer data.
• Experience with access controls, data governance, auditability, or similar security concepts.
• Understanding of compliance requirements is a strong plus, particularly SOC, SOX, GDPR, or financial-services regulations.
• Experience supporting ML infrastructure or AI workloads is highly valued.
• Familiarity with platforms such as SageMaker or Bedrock is a plus.
• Experience with real-time data processing is a plus.

Soft Skills

• Strong motivation for joining an early-stage startup.
• High ownership and ability to work independently.
• Strong problem-solving and systems-thinking skills.
• Comfortable operating in an ambiguous, fast-moving environment.
• Strong communication and collaboration skills.
• Ability to balance speed with reliability and engineering quality.
• Curious and motivated by technically challenging infrastructure problems.
• Clear understanding of why they want to work at an early-stage company.

Compensation & Benefits

• $150,000 - $300,000 base salary.
• Flexibility for exceptional candidates.
• Competitive equity package.
• Full-time position.
• On-site work in San Francisco or New York City.
• Flexible working hours with no rigid office schedule.
• Opportunity to work alongside experienced founders and engineers.
• Significant technical ownership in a rapidly growing AI infrastructure company.

Why Join

• Join an early-stage AI infrastructure company backed by top-tier investors.
• Work on infrastructure supporting sensitive, high-value financial data.
• Build systems at the intersection of cloud infrastructure, data engineering, security, and AI.
• Work alongside experienced founders with backgrounds at high-growth technology companies.
• Join as one of the early engineering hires and have meaningful influence on the platform's architecture.
• Solve challenging infrastructure problems involving large-scale data ingestion, AI workloads, security, and multi-cloud deployments.
• Work in an environment that values both high technical standards and flexibility.
• Help unlock additional customer growth by building the infrastructure needed to scale the business.