Taskrabbit

HQ
San Francisco
Total Offices: 2
450 Total Employees
Year Founded: 2008

Taskrabbit Offices

Taskrabbit is headquartered in San Francisco and has 2 office locations.

Hybrid Workplace

Employees engage in a combination of remote and on-site work.

Taskrabbit is a hybrid workplace which means we prioritize flexibility and work from home, but equally value coming together to connect throughout the year. Employees will be in a dedicated hub office 2 days per week in either SF, NYC or London.

Typical time on-site: 2 days a week

U.S. Office Locations

HQ

San Francisco

San Francisco, CA, United States

New York

New York, NY, United States

Recently posted jobs

2 Days AgoSaved
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Hybrid
San Francisco, CA, USA
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eCommerce • Information Technology • Sharing Economy • Software
Lead data science initiatives supporting Product and Commercial Operations. Analyze growth opportunities, define success metrics, conduct experimentation and causal inference, and deliver actionable insights. Lead initiatives such as experimentation systems and data agents while promoting strong analytical practices. Partner with stakeholders to drive measurable product impact, minimize marketplace losses, and communicate complex findings to executive audiences.
2 Days AgoSaved
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Hybrid
San Francisco, CA, USA
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eCommerce • Information Technology • Sharing Economy • Software
Drives platform modernization across engineering teams by applying target-state architecture, AI-assisted development, and Spec-Driven Development. Partners directly with engineers and technical leads, contributes code and technical designs, guides migration strategy, reviews TDDs and RFCs, establishes engineering standards, and coaches teams on scalable backend, distributed-system, and event-driven architecture practices.
3 Days AgoSaved
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Hybrid
2 Locations
Easy Apply
eCommerce • Information Technology • Sharing Economy • Software
Lead the end-to-end development and operation of machine learning systems supporting customer retention, ranking, matching, recommendations, pricing, and lifetime value growth. Build scalable data pipelines and ML infrastructure across batch and real-time environments, while implementing model monitoring, observability, deployment, and performance optimization. Collaborate across engineering and science teams and promote strong software engineering practices.