Company
AI startup
SolutionsEngineer
Neural analysis suggests this role is
optimal for Mid candidates.
“Solutions Engineer. Skills: Technical anchor of every enterprise deal, Owning discovery, demos, pilots, and production deployments end-to-end, Deploy and configure extraction pipelines inside customer environments, Diagnose and resolve accuracy, latency, and infrastructure issues across distributed systems, Build Python tooling and customer-facing utilities. Own all technical touchpoints in the sales cycle — discovery, demos, proof-of-concept evaluations, and production rollouts. Deploy and conf”
What You'll Achieve.
Become the document intelligence layer that the world's most sophisticated enterprises run on; Every complex, unstructured document becomes reliable, structured data; Your fingerprints will be on every major customer win
Industry & Context.
Diagnose and resolve accuracy, latency, and infrastructure issues across distributed systems
Must not require visa sponsorship
What They're Looking For.
Must Have
3–7 years in a customer-facing technical role — Solutions Engineer, Forward Deployed Engineer, or Implementation Engineer at a technical SaaS company, Early-stage startup experience (Seed–Series B), Solid grip on sales methodology — MEDDIC, Command of the Message, or you know how deals get stuck and how to unstick them, Must not require visa sponsorship
Nice to Have
Big Tech background considered only if paired with genuine startup and B2B-focused experience, Hands-on with APIs, distributed systems, and production Kubernetes experience is a plus, Background in data infrastructure, ML platforms, or document processing is a meaningful plus
What You'll Do.
Own all technical touchpoints in the sales cycle — discovery
proof-of-concept evaluations
and production rollouts
Deploy and configure extraction pipelines inside customer environments
from pilots through to enterprise-scale production
Diagnose and resolve accuracy
and infrastructure issues across distributed systems
Build Python tooling and customer-facing utilities to support integrations and downstream workflows
Sit at the intersection of customer
and product — funnel real signal back into the roadmap
How You'll Work.
Team & Collaboration
Working shoulder-to-shoulder with AEs; Directly alongside the founders; Sit at the intersection of customer, ML, platform, and product
Communication Scope
Translate technical details into business value
Full Job Description
ABOUT THE COMPANY Our client is a YC-backed AI startup solving one of enterprise data's most stubborn problems: getting accurate, structured information out of complex documents at scale. Their breakthrough platform combines intelligent schema mapping with fine-tuned extraction models — doing what legacy OCR and traditional parsing tools consistently fail to do. They're processing over 1 billion pages for Fortune 50 companies and top global private equity firms, backed by Sequoia Scout, Y Combinator, Daniel Gross, and Nat Friedman. THE MISSION To become the document intelligence layer that the world's most sophisticated enterprises run on — where every complex, unstructured document becomes reliable, structured data. THE OPPORTUNITY This isn't a support role dressed up as Solutions Engineering. You'll be the technical anchor of every enterprise deal — owning discovery, demos, pilots, and production deployments end-to-end, working shoulder-to-shoulder with AEs and directly alongside the founders. At a 33-person company with real enterprise traction, your fingerprints will be on every major customer win. Tech stack: Python, Kubernetes, APIs, Distributed Systems, AWS, GCP, Azure, Docker, Helm, Terraform WHAT YOU'LL DO - Own all technical touchpoints in the sales cycle — discovery, demos, proof-of-concept evaluations, and production rollouts - Deploy and configure extraction pipelines inside customer environments, from pilots through to enterprise-scale production - Diagnose and resolve accuracy, latency, and infrastructure issues across distributed systems — be the person customers trust when things get hard - Build Python tooling and customer-facing utilities to support integrations and downstream workflows - Sit at the intersection of customer, ML, platform, and product — funnel real signal back into the roadmap WHAT YOU BRING - 3–7 years in a customer-facing technical role — Solutions Engineer, Forward Deployed Engineer, or Implementation Engineer at a technical Saa
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