Senior Software Backend Engineer, Market Pricing

🏢 Paveakatroveinformationtechnologies · 📍 San Francisco, CA & New York, NY 💰 $195,500 - $264,500 / yr

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Position Summary & Responsibilities

Who We Are

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 9,000+ companies unparalleled confidence in every compensation decision.

Top tier companies like OpenAI, McDonald’s, Instacart, Atlassian, Affirm, Ericsson, Synopsys, Stripe, Doordash, Fanatics, 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 80% 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 $175M in funding from leading investors like Andreessen Horowitz (a16z), Index Ventures, Y Combinator, Insight Partners, Atomico, Venture Partners, and Craft Ventures.

The Market Pricing Team @ Pave

Compensation decisions get made every day with far less data than they deserve. Market Pricing empowers teams to price jobs in hours instead of months, with more data, build competitive pay ranges, and understand exactly how those decisions land across their organization. Getting this right means companies can stay competitive and retain their best talent.

What You'll Do

Propose and implement scalable systems for our most sophisticated customers
Partner with stakeholders to take ambiguous problems from an idea to a shipped, enterprise-ready feature
Own all phases of the development lifecycle, from a conversation with a customer to post-release monitoring
Set technical direction across projects, mentor engineers, and raise the bar on

🎯 Candidate Preparation Guide For Paveakatroveinformationtechnologies Applicants

Key Role Competencies & Evaluation Highlights

Key Role Focus:

Measuring compensation data intelligence, enterprise SaaS compensation workflows, and secure data handling standards.

📋 Preparation Priorities (Key Areas to Clarify):

1. Treating compensation data carelessly without strict confidentiality protocols
2. Failing to understand pay equity, compliance, and labor market dynamics
3. Building rigid systems that cannot adapt to complex merit matrix configurations

✨ Recommended Role Alignment Hook:

I engineer intelligent compensation infrastructure that turns messy labor market data into clear, equitable, and strategic pay strategies.

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