Solutions Engineer

Lavendo • San Francisco, CA

1d • Hybrid

About The Position

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

Requirements

• 3–7 years in a customer-facing technical role — Solutions Engineer, Forward Deployed Engineer, or Implementation Engineer at a technical SaaS company (not a simple SaaS; your customers had real infrastructure questions)
• Early-stage startup experience (Seed–Series B); Big Tech background considered only if paired with genuine startup and B2B-focused experience
• Solid grip on sales methodology — MEDDIC, Command of the Message, or equivalent; you know how deals get stuck and how to unstick them
• Hands-on with APIs, distributed systems, and production infrastructure; Kubernetes experience is a strong plus
• Proficient in Python — you write tools, not just read them
• Must not require visa sponsorship — the company cannot sponsor at this stage

Nice To Haves

• Background in data infrastructure, ML platforms, or document processing is a meaningful plus

Responsibilities

• 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

Benefits

• Medical, vision, and dental
• Daily meal stipend
• Relocation assistance for Bay Area moves

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level
• Education Level: No Education Listed