Solutions Engineer, Unstructured.io
Post-Sales and ProServe
$185-245k
Data prepping for Large Language Models
Open for applications
Data prepping for Large Language Models
21-100 employees
Open for applications
$185-245k
21-100 employees
Company mission
To give organizations access to all of their data.
Role
Who you are
- 5+ years of experience in post-sales engineering, solutions architecture, or technical consulting
- Deep experience with cloud-native architectures (AWS, Azure, GCP), containers (Docker, Kubernetes), and IaC tools
- Strong hands-on Python skills for scripting, API integration, and automation
- Proven success leading enterprise-scale deployments and solving real customer problems with technical precision
- Excellent communicator—you simplify the complex and build trust across both engineering and business audiences
- Comfort navigating ambiguity and speed—especially in startup or high-growth environments
Desirable
- Experience delivering or optimizing RAG pipelines, vector databases, or LLM-based applications
- Knowledge of DevSecOps best practices and commercial security/compliance frameworks (SOC 2, ISO 27001)
- Familiarity with both pre-sales and post-sales solutioning across AI/ML workflows
- Startup DNA—you move fast, stay scrappy, and take ownership end-to-end
What the job involves
- We’re entering our next chapter of growth—and we’re hiring a Solution Engineer (Post-Sales/ProServe) to help bring our commercial customers from purchase to production with ease
- You’ll be a core part of our commercial team, leading enterprise deployments, building scalable cloud-native architectures, and ensuring customers unlock the full potential of our platform
- Design and deliver secure, scalable post-sales implementations for commercial customers—across AWS, GCP, and Azure
- Guide customers through architecture, integration, optimization, and tuning—especially around AI/LLM and RAG use cases
- Create reference architectures, onboarding guides, and playbooks that reduce time-to-value and accelerate adoption
- Run enablement sessions, whiteboarding workshops, and POCs to maximize customer value and foster long-term partnerships
- Solve complex infrastructure and data pipeline issues—ensuring stability, observability, and high performance
- Feed customer insights directly to Product & Engineering to shape our roadmap and improve enterprise capabilities
Our take
Unstructured.io is tackling a prevalent challenge in AI adoption - the difficulty of integrating complex, enterprise-sized data with Large Language Models (LLMs). The company's platform provides a solution by extracting and staging all data, even that which is in difficult-to-use formats, and making it compatible with LLMs.
Thus, the startup streamlines the time-consuming data preparation process, providing tools for otherwise complex tasks like removing unwanted elements, text concatenation, and Optical Character Recognition (OCR). It also tailors processing pipelines for specific document types, addressing a critical bottleneck in AI development workflows.
In enabling the seamless integration of LLMs with enterprise data, Unstructured.io's platform has the potential to unlock substantial productivity gains. As of 2023, it boasted over 700,000 downloads, and will continue to develop its pioneering solution to the data preparation bottleneck which will be critical for addressing evolving industry demands.
Steph
Company Specialist at Welcome to the Jungle
Company
Funding (last 2 of 3 rounds)
Mar 2024
$40m
SERIES B
Jul 2023
$20m
SERIES A
Total funding: $65m
Company HQ
SoMa, San Francisco, CA
Leadership
Brian S. Raymond
(CEO)Previously worked in Investment Banking at Harris Williams & Co. Prior to this they worked at The White House in the National Security Council and as an Intelligence Officer at the Central Intelligence Agency.
Matt Robinson
(Head of Product)Previously a Machine Learning Engineer at Rebellion Defense, a Solutions Engineer/Director at Primer.ai, and a Data Science Manager at Capital One.
Crag Wolfe
(Head of Engineering)Previously the Director of Engineering at Primer.ai. They also worked at Red Hat as a Python Developer and at Ricoh Innovations as a Web Architect.
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