Pave
Senior Software Engineer, Product
San Francisco, CA & New York, NY · Senior
Sponsorship not specified$196k-$265kDetected 21 days ago
TypeScriptNode.jsFull-Stack DevelopmentDatabricksMachine LearningCompensationLeadershipCollaboration
About the role
- The future of pay is real-time & predictive, and we're making it happen right now.
- Learn more about our engineering principles here!
Responsibilities
- you influence design decisions across the team, mentor other engineers, and bring people along through clear reasoning
Nice to have
- experience at a B2B SaaS company, especially during a period of rapid growth or platform expansion
- Compensation, It's What We Do.
- We're always happy to walk you through how we think about leveling - just ask.
- Targeted cash compensation for this role:
- P4: $195,500 - $264,500
- Fuel for the Work: Lunch and dinner stipends plus fully stocked kitchens, so you can stay energized without thinking twice about it.
- And we're just getting started.
Compensation
- As you develop, your role expands, your responsibilities deepen, and your compensation reflects the impact you're making.
- 5+ years of full-stack, backend, or frontend engineering experience, ideally with modern web technologies such as TypeScript and Node.js, including experience building data-intensive applications.
- P4: $195,500 - $264,500
Benefits
- Your Health, Fully Covered: Comprehensive medical, dental, and vision coverage for you and your family, with a range of options designed to meet you where you are.
- Time That's Actually Yours: Flexible PTO and the freedom to work from anywhere in the world for up to a month - because life doesn't pause, and neither should you.
- Room to Keep Growing: A quarterly education stipend to invest in the skills and knowledge that matter most to you.
- Support When It Matters Most: Robust parental leave so you can be fully present for the moments that count.
- Getting Here, Made Easier: A commuter stipend to support the in-person collaboration that makes great work happen.
Company info
- At Pave, we're building the industry's leading compensation platform, combining the world's largest real-time compensation dataset with deep expertise in AI and machine learning. Our platform is perfecting the art and science of pay to give 8,500+ companies unparalleled confidence in every compensation decision.
- Top tier companies like OpenAI, McDonald's, Instacart, Atlassian, Synopsys, Stripe, Databricks, and Waymo use Pave, transforming every pay decision into a competitive advantage. $190+ billion in total compensation spend is managed in our workflows, and 70% of Forbes AI 50 use Pave to benchmark compensation.
- The future of pay is real-time & predictive, and we're making it happen right now. We've raised $160M in funding from leading investors like Andreessen Horowitz, Index Ventures, Y Combinator, Bessemer Venture Partners, and Craft Ventures.
- Product @ Pave
- breakfast, new hire welcomes, product updates, fireside chats, and yes, the occasional Kahoot.
- It's one of the things people notice when they join us - that we truly enjoy spending time together.
- Be Intellectually Honest - Truth over comfort. We face reality clearly and speak directly, even when it's hard.
- Play to Win - We're not here to participate. We're here to be the #1 compensation platform in the world, and we act like it.
- Uphold the Pave Platinum Standard - We hold ourselves to the highest bar - for our customers, our data, and each other.
- One Team - We win and lose together. Titles don't drive decisions here - shared goals do.
- Hug of Jawn - Hard to define, impossible to miss. Ask your recruiter.
- Build confidence in every compensation decision.
- We build software that transforms how companies pay their people - and we believe the team behind that software deserves the same thoughtfulness.
- If you're ready to help shape the future of compensation alongside people who are smart, humble, and genuinely motivated by the problem we're solving, we'd love to meet you.
This listing is sourced directly from Pave's careers page and normalized into a canonical job model.