Founding Infrastructure Engineer

Reducto helps AI teams ingest real world enterprise data with state of the art accuracy.

The vast majority of enterprise data — from financial statements to health records — is locked in unstructured file formats like PDFs and spreadsheets. We train vision models to read those documents the way a human would, and make it possible to build products, train models, and automate processes at scale.

We’ve grown incredibly quickly, growing revenue by 7x YOY, and now work with hundreds of companies ranging from leading AI teams (Harvey, Vanta, Scale), through to enterprise (FAANG, top 3 trading firm).

We've raised over $100M from world‑class investors like a16z, Benchmark, and First Round Capital, and are hiring a founding Infrastructure Engineer.

As the first dedicated Infrastructure Engineer at Reducto, you will influence every aspect of our infrastructure from the ground up. You will architect and scale resilient systems for AI and ML workloads, automate cloud infrastructure, and implement monitoring and incident response practices that set the standard for reliability. This role requires technical leadership, hands‑on systems engineering, and strong collaboration with our founders and product teams as we build a company around reliability, rapid iteration, and high‑impact product delivery.

• Designing, building, and maintaining highly available, scalable infrastructure to support intensive AI/ML workloads and real‑time model deployments.
• Implementing robust monitoring, alerting, and observability systems to ensure system health, performance, and uptime across cloud and on‑prem environments.
• Debugging, optimizing, and automating infrastructure for fast iteration and rapid deployment cycles, focusing on both reliability and developer velocity.
• Proactively identifying, investigating, and resolving incidents to minimize downtime and maintain world‑class service levels for enterprise customers.
• Collaborating closely with engineers, ML specialists, and founders to shape product, infrastructure, and security strategies.

• Are your own worst critic—have an extremely high bar for quality and always aim for robust solutions rather than quick fixes.
• Have 5+ years of hands‑on experience in building or supporting production‑grade infrastructure and reliability processes for high‑throughput systems.
• Are comfortable with Python or similar languages, and exceptional at working across cloud platforms, container orchestration (e.g., Kubernetes), networking, and storage technologies.
• Build your own tools on the fly to diagnose, experiment, and address reliability problems—whether it's an internal dashboard or an automated remediation workflow.
• Bring a quantitative, hands‑on approach to system operations, automation, and continuous improvement.
• Have prior experience founding a company or building products/infrastructure in early‑stage, high‑growth environments.
• Are excited about automating incident management processes with LLMs/AI.
• Are driven, ambitious, and deeply care about both technical excellence and collaborative problem‑solving.
• Keep up with the latest trends in cloud, observability, and SRE best practices.
• Are passionate about open‑source and have contributed tools or automation to reliability communities.
• Have built or optimized monitoring, incident response, or high‑performance computing systems for demanding AI/ML, fintech, or enterprise clients.

This is an in‑person role at our office in SF. We’re an early stage company which means that the role requires working hard and moving quickly. Please only apply if that excites you.

Nearly 80% of enterprise data is in unstructured formats like PDFs.

PDFs are the status quo for enterprise knowledge in nearly every industry. Insurance claims, financial statements, invoices, and health records are all stored in a structure that’s simply impractical for use in digital workflows. This isn’t an inconvenience—it’s a critical bottleneck that leads to dozens of wasted hours every week.

Traditional approaches fail at reliably extracting information in complex PDFs. OCR and even more sophisticated ML…