Forward Deployed Engineer, Infrastructure Specialist

About the role

As a Forward Deployed Engineer, Infrastructure Specialist you will lead the end-to-end deployment of Reducto into customer environments, ranging from VPC to bare metal on-prem. Each deployment comes with its own hardware, networking, security, and reliability constraints, and you will be the engineer who makes them work. This role sits at the bridge between Reducto and our enterprise customers' infrastructure, security, and platform teams. You will partner directly with those teams to deploy, configure, harden, and operate Reducto inside their environments. This is hands-on execution work with direct customer exposure, not a bottoms-up platform role. The core work will include: Leading end-to-end deployment of Reducto into customer environments including planning, configuration, testing, and rollout. Partnering with enterprise IT, security, and platform teams to assess their infrastructure, security posture, and data management practices, and designing deployment strategies tailored to their constraints. Understanding the hardware that power our in-house ML models so we can successfully deploy them onto customer infrastructure. Building monitoring, telemetry, and phone-home patterns that give us visibility into customer-controlled environments without compromising their security posture. Debugging and resolving customer-specific infrastructure issues end-to-end, like K8s misconfigurations and cloud IAM edge cases, minimizing customer downtime. Owning incident response for customer deployments with world-class operational excellence. Codifying what works into repeatable deployment patterns, runbooks, and automation - turning one-off customer work into infrastructure that scales across the next ten deployments. We would love to meet you if you: 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. Bonus points if you: 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. About Reducto 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. Ov