Menlo Ventures
Founding Engineer - Full Stack, Backend Leaning
Menlo Ventures, San Francisco, California, United States, 94110
Founding Engineer - Full Stack, Backend Focus
San Francisco Bay Area NegotiateAI - Engineering / Full-time / Hybrid NegotiateAI is a portfolio company of Menlo Ventures. 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 $130,000 - $180,000 based on experience and equity preference Pre-seed level equity grant based on cash preference Medical, Dental, Vision $130,000 - $180,000 a year Plus pre-seed equity grant Apply for this job
San Francisco Bay Area NegotiateAI - Engineering / Full-time / Hybrid NegotiateAI is a portfolio company of Menlo Ventures. 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 $130,000 - $180,000 based on experience and equity preference Pre-seed level equity grant based on cash preference Medical, Dental, Vision $130,000 - $180,000 a year Plus pre-seed equity grant Apply for this job