Jobs at Taskrabbit

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.

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.

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.

Manage Tasker operations across assigned metropolitan areas, using supply and marketplace data to improve activation, booking conversion, quality, retention, and revenue. Coach Taskers, lead lifecycle and community programs, design experiments, communicate insights, validate product issues, and collaborate cross-functionally with Product, Support, Marketing, Legal, and Partnerships. The role requires English and Spanish fluency, strong data skills, and up to 10% travel.

Own and architect the core ranking system end-to-end: model design, production reliability, automated retraining, bias mitigation, personalization, and cross-functional technical leadership. Mentor engineers, drive roadmap, and scale experimentation and ML infrastructure.

Lead development and scaling of Taskrabbit’s global brand and content strategy. Build messaging architecture, manage cross-channel content programs, partner with Creative/SEO/Growth to produce scalable formats, prototype campaigns, and measure performance to drive awareness, engagement, and long-term demand.

Build and maintain ELT pipelines, data models, semantic layers, and personalization signals for client retention and marketing tests. Partner with machine learning, product, marketing, design, and architecture teams to prepare predictive data, connect signals to CRM channels, support real-time event processing, and document a scalable data foundation. Use AI coding assistants daily to accelerate development, testing, and reviews in a fast-moving discovery environment.

Lead architecture and complex cross-team initiatives for Taskrabbit’s Tasker and Supply marketplace systems. Build onboarding, eligibility, geography, and matching capabilities using Ruby on Rails and Sorbet, while advancing strongly typed TypeScript service contracts. Improve reliability, observability, and alerting with Kafka and Datadog; resolve difficult production issues, reduce technical debt, mentor engineers, promote testing and AI-driven development, and influence technical strategy across teams.

Lead and grow a security engineering team as a technical player-coach; implement security tooling and automation across corporate, infrastructure, and application layers; define data-driven security strategy, OKRs, and KPIs; manage compliance with frameworks (SOC2, CIS, GDPR); lead incident response and threat modeling; govern AI/ML security risk; and drive security culture and cross-functional education.

The AI Automation Senior Lead will build AI infrastructure, evaluate AI tools, redefine workflows, and maximize efficiency across teams at Taskrabbit.

Lead cross-functional analytical efforts to drive product, commerce, and risk outcomes. Design experiments, causal inference, and statistical models; build and scale fraud/risk interventions using heuristics and machine learning; measure feature impact; and communicate actionable insights to stakeholders and executives.

Lead Post-Booking engineering efforts: define technical roadmap, architect and implement Node.js microservices, drive AI-driven and spec-driven development, mentor engineers, own observability, and modernize legacy systems to improve customer experience.