Infrastructure Engineer (San Francisco / New York City, On-Site)

Compensation: $150,000–$300,000 USD + Equity

Company Size: Early-stage | Join as one of the first 20 engineers

Company Funding: $50M Series A | Backed by a top-tier Silicon Valley venture firm

Tech Stack:

AWS, GCP, Azure, Kubernetes, Docker, Terraform, Pulumi, Datadog, Spark, Kafka, Databricks, CI/CD, AI/ML Infrastructure, LLM Infrastructure

Why This Role

This is not a traditional DevOps role.

This is an opportunity to build the infrastructure foundation of a well-funded Series A AI company as one of its first 20 engineers.

The company is building an enterprise AI data platform for financial services, with infrastructure spanning cloud deployments, large-scale data ingestion, security, and AI/LLM workloads. Customer demand is already exceeding engineering capacity, creating an opportunity to build systems that will immediately support real production growth.

You'll have genuine early-stage ownership with the funding, customer traction, and experienced leadership needed to build at serious scale. The founders have previously helped scale category-defining technology companies from fewer than 50 employees through hypergrowth.

You will:

• Design and build scalable cloud infrastructure across AWS, GCP, and Azure.
• Build and operate Kubernetes and containerized production environments.
• Develop infrastructure supporting secure private cloud and BYOC deployments.
• Build complex data ingestion and ETL pipelines for structured and unstructured financial data.
• Implement infrastructure-as-code using Terraform, Pulumi, or similar tooling.
• Build and improve CI/CD, monitoring, logging, alerting, and observability systems.
• Design security-first infrastructure incorporating access controls, audit trails, tenant isolation, and data governance.
• Partner closely with AI Research and Product teams on LLM inference, training, GPU workloads, and agent infrastructure.
• Own major infrastructure projects from initial architecture through production deployment and scale.
• Establish technical foundations and infrastructure patterns that can support the company through its next stages of growth.

Skills:

• 4–5+ years of cloud, platform, infrastructure, DevOps, SRE, or data infrastructure engineering experience.
• Strong expertise with AWS, GCP, or Azure.
• Strong production experience with Kubernetes and Docker or similar container technologies.
• Experience with Terraform, Pulumi, or other infrastructure-as-code tooling.
• Experience building CI/CD pipelines and production deployment infrastructure.
• Experience building data ingestion, ETL, or large-scale data pipelines.
• Experience with technologies such as Spark, Kafka, Databricks, or comparable data infrastructure.
• Experience with monitoring and observability platforms such as Datadog, Prometheus, or Grafana.
• Demonstrated 0→1 infrastructure ownership and experience building systems from scratch.
• Strong understanding of production reliability, scalability, security, and operational excellence.
• Experience working in a high-growth startup or another sophisticated, high-performing engineering environment.
• Strong Computer Science academic background.
• Ability to work on-site in San Francisco or New York City.

Other Skills:

• Experience building AI/ML infrastructure.
• Experience supporting LLM inference or training workloads.
• Experience building GPU infrastructure or infrastructure for AI agents.
• Multi-cloud experience across AWS, GCP, and Azure.
• Experience with SageMaker, Bedrock, or similar cloud-native AI/ML platforms.
• Experience building private cloud or BYOC enterprise deployments.
• Experience designing infrastructure for multi-tenant enterprise software.
• Experience with financial services or another highly regulated industry.
• Experience with SOC, SOX, GDPR, security, compliance, or data governance requirements.
• Experience building infrastructure for sensitive or highly regulated datasets.

How The Company Works

• Early-stage ownership – Join as one of the first 20 engineers and help establish the infrastructure architecture from the ground up.
• Infrastructure is core to the product – Cloud, data, security, and AI infrastructure are fundamental to what enterprise customers are buying.
• Strong customer pull – Customer demand is already exceeding the engineering team's current capacity, creating immediate opportunities for impact.
• Hard technical problems – Engineers work across multi-cloud infrastructure, sensitive financial data, large-scale data pipelines, LLM workloads, and enterprise-grade security.
• Experienced leadership – The founders have previously helped scale category-defining technology companies through periods of significant growth.
• 0→1 engineering – Engineers build foundational systems and make architectural decisions rather than simply maintaining mature infrastructure.
• High ownership, high trust – Engineers have significant autonomy and responsibility for taking projects from architecture through production.
• Cross-functional engineering – Infrastructure works closely with AI Research and Product to support both customer-facing systems and advanced AI workloads.
• On-site collaboration – The engineering team works on-site in San Francisco or New York City with flexible working hours.