Forward Deployed Engineer, Infrastructure Specialist

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 operating production infrastructure, with meaningful time spent deploying software into environments you don't fully control.

Are comfortable with Python or similar languages, and exceptional at working across cloud platforms, container orchestration (e.g., Kubernetes), networking, and storage technologies.

Communicate clearly with external engineers. You can sit in a call with a customer's platform team, diagnose a problem live, and come out with a path forward that both sides trust.

Are energized by being assigned to a customer problem and owning it end-to-end.

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 deployed software into regulated environments (financial services, healthcare, legal, insurance) with complex infrastructure.

Have worked with Replicated/KOTS, Helm, or similar enterprise distribution tooling.

Have experience with GPU infrastructure, model serving, or AI/ML workload deployment patterns on customer-controlled hardware.

Have prior experience at an early-stage, high-growth company — as a founder, founding engineer, or early deployment hire.

Are driven, ambitious, and deeply care about both technical excellence and collaborative problem-solving.

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 approaches work for simple text documents but are unreliable for anything more complex. Text from different columns are jumbled together, figures are ignored, and tables are a nightmare to get right. Overcoming this usually requires a large engineering effort dedicated to building specialized pipelines for every document type you work with.

Reducto breaks document layouts into subsections and then contextually parses each depending on the type of content. This is made possible by a combination of vision models, LLMs, and a suite of heuristics we built over time. Put simply, we can help you:

Accurately extract text and tables even with nonstandard layouts

Automatically convert graphs to tabular data and summarize images in documents

Extract important fields from complex forms with simple, natural language instructions

Build powerful retrieval pipelines using Reducto’s document metadata

Intelligently chunk information using the document’s layout data