# Staff Machine Learning Engineer - Ranking System at TaskRabbit

- Company: TaskRabbit
- What the company does: Our same-day service platform instantly connects you with skilled Taskers to help with cleaning, furniture assembly, home repairs, running errands and more. Backed by Lightspeed and 500 Global.
- Company website: https://taskrabbit.com/
- Type: Startups (AI role)
- Level: Senior
- Location: New York, New York, United States; San Francisco, California, United States
- Work setup: On-site
- Pay: $170K to $225K base salary per year (USD)
- Posted: 2026-06-25
- Apply by: 2026-10-08
- Apply: https://job-boards.greenhouse.io/taskrabbit/jobs/8027537
- Page: https://www.1752.vc/careers/jobs/taskrabbit-staff-machine-learning-engineer-ranking-system/

## About the role

Machine Learning is a cornerstone at Taskrabbit, and we’re looking for a Staff Machine Learning Engineer to take technical ownership of our core ranking system. Every job request on the platform flows through it, making this one of the most consequential ML systems we run.

## What they're looking for

- BS, MS, or PhD in Computer Science, Statistics, Operations Research, or a related quantitative field
- 8+ years of industry experience building and deploying production-grade ML systems, with a track record of owning a system end-to-end — not just contributing to one
- Deep expertise in ranking, recommender systems, or two-sided marketplace ML. Experience with debiasing, A/B experimentation at scale, and the subtleties of feedback loops in production systems
- You think architecturally. You can hold the full picture of a complex system — data pipelines, feature stores, training infrastructure, serving, monitoring — and make principled decisions about how they fit together
- You write and review code at a high standard. Your code reviews are insightful and educational, your implementations set the bar for quality and maintainability on the team
- You can do critical R&D. When a problem is novel and the solution is unclear, you have the depth to explore it systematically and the judgment to know when to go deeper vs. ship

Tags: Data