Machine Learning Engineer

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 58% 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.

Pave's R&D pillar includes our data science, engineering, information technology, product design, product management, and security teams. This organization builds, maintains, and secures a platform used by more than 8,500+ client organizations.

Our engineering team moves between ideation, scoping, and execution in a matter of days while closely iterating with cross‑functional partners on requirements. At Pave, we use Type Script, Node.js, and React, hosted on GCP. Compensation strategy is broken down into three pillars –
compensation bands
, planning workflows
, and total rewards communication
. We build products that make these processes seamless for customers.

Over the next year, our roadmap is focused on enhancing the entire compensation lifecycle: from philosophy definition to market trend analysis, band adjustments, merit cycles, and employee communication. We’re seeking passionate engineers who are excited about building robust, data‑rich systems that simplify complex compensation processes at scale.

As part of the Data team at Pave, you will help us redefine how companies understand the labor market and determine compensation. Even the most innovative tech companies in the world often use spreadsheets full of flawed statistics to determine how to pay. At Pave, we’ve built a system of real‑time integrations that allow us to bring best practices from machine learning, data science, software tooling, and AI to an industry that is built on data, but doesn’t have the tools it needs to fully leverage it.

• Architect and implement scalable ML systems for modeling compensation within a single company and across the market as a whole
• Collaborate with product and engineering teams to identify additional opportunities to leverage ML‑driven solutions
• Help evolve the technical direction of ML initiatives across the company
• Drive millions of dollars of revenue growth

• 5+ years of experience building and deploying ML models in production environments
• Strong foundation in machine learning, statistics, and deep learning fundamentals
• Expertise in Python and modern ML frameworks (PyTorch, Tensor Flow, or similar)
• Experience with large‑scale data processing and ML model optimization
• Experience with MLOps practices and tools (model versioning, monitoring, and deployment)
• Strong software engineering practices and experience with production systems
• Expert‑level SQL skills with experience writing complex queries and optimizing query performance
• Ability to navigate (and bring structure to) ambiguity; ability to bring a project from 0 to 1, or scale a project from 1 to 100

Salary is just one component of Pave’s total compensation package for employees. Your total rewards package at Pave will include equity, top‑notch medical, dental, and vision coverage, a flexible PTO policy, and many other region‑specific benefits. Your level is based on our assessment of your interview performance and experience, which you can always ask the hiring manager about to understand in more detail.

This salary range may include multiple levels.

The targeted cash compensation for this position is (level depends on experience and performance in the interview process):

P3: $195,000 – $215,000
P4: $230,000 – $250,000

Since…