Founding Engineer

Dealops•San Francisco, CA

10h•$140,000 - $180,000•Hybrid

About The Position

We’re building the revenue infrastructure the next decade of B2B AI companies run on by starting with the most critical part in the revenue journey, deal pricing. Today, sales reps spend hours building their own pricing spreadsheets for each deal, only to discount too much and spend another cycle iterating with Finance. This is a massive problem for B2B companies, as we’ve seen up to 30% unnecessary overdiscounting with customers. Our founder saw this firsthand when she worked in finance and pricing at Stripe, where she has priced over a thousand deals with the sales org. This problem exists because pricing is a black box. Sales teams aren’t trained to make pricing decisions and aren’t incentivized to optimize for long-term revenue. As pricing models become more complex due to AI and the shift toward usage-based, outcome-based, and hybrid models, this challenge is only compounding. There’s a massive opportunity to build the revenue infrastructure for next-generation companies. We’re positioned to lead this market shift as teams are finding out previous solutions are breaking down. That’s why the fastest growing enterprises and AI startups like Airwallex, Plaid, Harvey, LangChain, and Clay are partnering with us to build the foundations of their revenue infrastructure. Backed by $7M from General Catalyst, Pear VC, and executives from OpenAI, Stripe, Slack, and more, we’re now focused on growing revenue 10x and shaping the future of deal pricing.

Requirements

4+ years of software development experience, with a strong full-stack focus (senior preferred; strong mid-level welcome).
Proficiency in React, TypeScript, and SQL (PostgreSQL or similar).
A track record of building and launching scalable, reliable products in close collaboration with designers and other cross-functional partners.
Experience with complex products that have high reliability and accuracy requirements, ideally in a fast-moving environment.

Nice To Haves

Experience with Tailwind, Express, machine learning, prompt engineering / AI tooling, or previously leading a tech team.

Responsibilities

Develop key features for deal pricing and packaging recommendations, blending machine learning with a rule-based engine to optimize pricing.
Design and run A/B pricing experiments that directly move customer revenue — past experiments have yielded a 10–20% boost.
Collect and integrate customer feedback to drive iterative improvement and product-market fit.
Build agents that process unstructured, natural-language data to further sharpen our pricing and packaging recommendations.
Spearhead infrastructure and tooling for admin users, enabling infinite customization through natural language and LLMs which cuts customer onboarding time by 10x.
Contribute to the future product roadmap and our long-term vision of becoming an end-to-end sales optimization platform.
Own an entire product or product feature suite, from design through implementation and keep innovating.
Pitch in on customer onboarding and support.
Plan and lead projects end-to-end.