Top Tech Jobs & Startup Jobs

Leads a team of scientists developing data-driven pricing and incentive products for a complex marketplace. Responsibilities include formulating ambiguous business problems, building optimization, causal inference, and machine learning models, designing experiments, defining success metrics, guiding end-to-end product development, mentoring scientists, partnering with stakeholders, and communicating recommendations to senior leadership.

Lead operational readiness, launch execution, performance management, and change management for Uber’s AV Remote Assistance program. Develop implementation plans, readiness checklists, launch trackers, risk registers, operating cadences, metrics, and stabilization plans. Partner with Product, Engineering, Operations, Analytics, Training, Safety, and external partners to ensure reliable deployment and execution. Provide data-driven recommendations and executive-level communication in a high-ambiguity environment.

Lead the strategy and execution of generative AI products for riders, eaters, and drivers. Translate user needs into measurable features, partner with engineering, data science, and design teams, and launch and iterate AI-driven experiences. The role requires strong product management experience with machine learning products, technical fluency, data-driven prioritization, and excellent communication. Experience launching generative AI or agentic systems is preferred.

Leads Uber’s U.S. state and local income and franchise tax function, including ASC 740 tax provision, planning, M&A advisory, controversy, compliance guidance, legislative monitoring, process automation, provider management, and team development.

Advise business process owners on SOX compliance and internal controls over financial reporting. Evaluate control design and effectiveness, map financial assertions, manage deficiencies and remediation, support audits, deliver risk reporting and training, and drive process improvements through automation, data analytics, and AI. Mentor junior analysts and monitor regulatory developments, including PCAOB standards and SEC guidance.

Provides commercial and product legal counsel to Uber for Business. Responsibilities include drafting and negotiating enterprise sales, strategic relationship, promotional, privacy, and technology agreements; developing scalable sales-cycle policies; reviewing marketing and product materials; advising sales and business teams; and guiding product managers and engineers through product launches, legal reviews, and risk mitigation.

Own logistics execution for autonomous-vehicle hardware, including central warehouse operations, spares distribution, factory material readiness, supplier coordination, and contractor governance. Resolve shipping and warehouse disruptions, ensure on-time and damage-free delivery, and build automated inventory tracking and data workflows. Manage BOM and configuration changes, establish SOPs and audit metrics, and provide visibility into inventory, ETAs, and component readiness across factories and field garages.

Develop firmware and low-level drivers for automotive components and autonomous vehicle sensors. Lead custom hardware bring-up, validation, deployment, and hardware-software co-design. Debug complex embedded systems using hardware tools, optimize sensor data pipelines, and ensure timing and network synchronization. Contribute to production lifecycle execution, embedded architecture, code quality, and team mentoring.

Own the full sales cycle for local and regional small and medium restaurants, from prospecting and cold-calling through contract negotiation and partnership signing. Build and manage a pipeline, conduct in-person meetings, achieve sales quotas, communicate Uber Eats’ value, and collaborate with Account Management to support onboarding and long-term restaurant relationships.

Lead the technical strategy and architecture for foundation models powering search, recommendations, personalization, and conversational AI across Uber’s Mobility and Delivery products. Own cross-team initiatives involving retrieval, ranking, embeddings, and LLM assistants; guide build-versus-buy decisions; influence product strategy; deliver large-scale ML systems; and mentor senior engineers while connecting model improvements to business outcomes.