Software Engineer

About NegotiateAI (Menlo Ventures Backed)

Today we're building AI for category managers at manufacturing and industrial companies, taking the grind out of indirect spend and delivering enterprises millions in savings.

That's the starting point. Where we're going is general procurement AI that spans the entire function.

If you're excited about solving hard technical problems and building a transformative product early, we'd love for you to join us.

About this role

You'll be one of the first engineers on the team, building the systems that power our AI-driven procurement platform. You'll own meaningful pieces of the product end to end, and work directly with the founder and our earliest customers to figure out what to build next.

This is a hands-on role with a technical leadership component, but no direct reports.

What you'll do

• Build and own core backend services, including integrations with ERP systems, supplier databases, and spend analytics tools.

• Build the agentic systems at the core of the product: pipelines that categorize indirect spend and resolve part identifiers from incomplete supplier data, and agents that source online benchmarks, gather supplier contacts, and support automated negotiation.

• Ship customer-facing features for real-time spend visibility and supplier benchmarking, and iterate quickly on customer feedback.

• Build evaluation and monitoring for our AI systems so we can measure accuracy and catch regressions before customers do.

• Set architectural direction for the systems you own, and mentor engineers earlier in their careers.

• Apply security and data privacy practices, including anonymization, tokenization, and secure sandboxes for model work.

Must haves

• 5+ years of professional software engineering experience, with a track record of shipping production systems end to end.

• Strong backend engineering skills, including API design and data pipeline work. Depth matters more to us than experience in any particular language.

• Hands-on experience building with LLMs in production, including agentic workflows, tool use, context and retrieval strategies, and evaluation.

• Experience shipping against messy real-world data: cleaning, normalization, entity resolution, and establishing ground truth.