As a Founding Engineer, you'll design and build the core infrastructure for an AI-driven procurement platform, implementing advanced data solutions and ensuring security compliance.
NegotiateAI is a portfolio company of Menlo Ventures.
About NegotiateAI
We’re building a revolutionary enterprise platform to transform procurement. Our mission is to transform procurement into a core strategic function, starting with the manufacturing and industrial verticals. Our focus is to streamline indirect spend, delivering exceptional cost savings and operational efficiency to procurement teams everywhere. It’s time to bring innovation and efficiency to procurement.
As the founder, I come from a background in global sourcing and supply chain leadership, having grown and led organizations at Apple for nearly a decade. With backing from Menlo Ventures, we’re leveraging cutting-edge technologies like agentic AI, LLMs, and Retrieval-Augmented Generation (RAG) to build a platform that guarantees enterprises millions in savings on their indirect spend.
If you’re excited about solving complex technical challenges and building a transformative product from the ground up, we’d love for you to join us.
About this role
This foundational teammate will build the infrastructure that powers our AI-driven procurement platform, enabling seamless data categorization and harmonization, Agentic AI-driven benchmarks, and Agentic AI-driven negotiations. You will lay the groundwork for scalable, innovative solutions that transform how enterprises manage their spend.
Core Responsibilities
- Design and architect the foundational infrastructure for NegotiateAI’s procurement platform, enabling seamless integration with ERP systems, supplier databases, and spend analytics tools.
- Identify key technical requirements to optimize workflows for indirect spend categorization with RAG+LLMs, unique part identification extraction, agentic AI-driven benchmarking strategies, and agentic AI negotiation automation.
- Define fundamental platform capabilities that streamline procurement processes, including real-time spend visibility, supplier benchmarking, and automated decision-making.
- Implement LLM, RAG, and Agentic AI pipelines to process and analyze large-scale procurement data, generate actionable insights, and power intelligent spend recommendations.
- Apply data privacy principles and implement best practices for security, including data anonymization, key obfuscation, tokenization, training models within secure sandboxes, adhering to GDPR standards, and ensuring SOC II compliance.
- Train LLMs and implement agentic AI tailored to procurement, creating intelligent agents that find online benchmarks, clean and triangulate dirty data to find critical part information, find contact information from suppliers online, communicate with suppliers, reduce costs, and drive measurable value for enterprises.
Qualifications
- 5+ years experience in full-stack engineering, with senior-level expertise
- Experience building 0 → 1 in a startup, earlier stage the better (pre-seed, seed, series A)
- Strong track record of iterating quickly on product and growing a customer base
- Experience working with modern AI/ML technologies, including Agentic AI, LLMs, RAG and prompt engineering
- Tech Stack: Next.js (TypeScript), Supabase (PostgreSQL), Node.js, Langsmith
- Ability to code and iterate quickly and scrappily, with also being able to step back and architect the stack
- Data Science background or familiarity (key: working with large data sets, parsing)
- Hungry to build a $B+ VC-backed AI enterprise solution
Nice-to-haves
- Some large company experience early in career
- Experience building consumer-grade software
- Experience in scaling and working with enterprise clients
- Founding engineer experience at pre-seed through series A or B startup
Compensation and Benefits
- Base salary of $130,00 – $180,000 based on experience and equity preference
- Pre-seed level equity grant based on cash preference
- Medical, Dental, Vision
Top Skills
Langsmith
Next.Js
Node.js
Postgres
Supabase
Typescript
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