Top Tech Jobs & Startup Jobs in Seattle, WA

Leads scientific strategy and experimentation across Uber, translating ambiguous business problems into rigorous experiments and production solutions. Designs algorithms, establishes experimentation standards, improves technical performance, mentors scientists, and influences executive roadmaps. Requires deep expertise in statistical inference, experimental design, causal inference, and large-scale data tools, with responsibility for multi-team technical direction and critical product-area outcomes.

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.

Lead the architecture and evolution of Uber’s global core infrastructure, including compute, networking, orchestration, multi-cloud foundations, security, and automation. Design hyperscale distributed systems, improve fleet efficiency, support GPU and AI/ML workloads, enable regional failover, and integrate zero-trust networking. Lead complex technical initiatives from design through global rollout, collaborate across engineering teams, contribute to open-source infrastructure, and mentor engineers.

Own the strategy, roadmap, stabilization, and continuous enhancement of the Talent Acquisition people-systems ecosystem. Manage Oracle ORC and integrated SaaS and custom tools, including backlog prioritization, issue triage, PRDs, user stories, metrics, and cross-functional alignment. Partner with Talent Acquisition, engineering, development, UX, and global stakeholders to improve candidate and recruiter experiences while balancing delivery schedules, system performance, cost, and ROI.

Develop and own end-to-end machine learning models for Uber Freight’s marketplace, including cost prediction, booking probability, demand elasticity, pricing, matching, recommendations, and optimization. Collaborate with backend engineers to productionize models and ensure reliable performance. Apply predictive modeling, causal inference, constrained optimization, reinforcement learning, and marketplace design to solve business problems.

Perform P&C actuarial reserving for Uber’s commercial auto and other insurance coverages. Responsibilities include estimating unpaid claims liabilities, supporting quarterly reserving, validating data, analyzing claims, building visualization tools, making IBNR selections, implementing financial results, and presenting findings to actuarial, claims, accounting, finance, and insurance stakeholders.

Design and build scalable ML infrastructure and backend systems for high-impact marketplace applications. Lead architecture, solve complex low-latency and high-correctness problems, drive organization-wide framework adoption, and translate business requirements into technical roadmaps. Mentor engineers, promote engineering excellence, and develop or support ML models, foundational models, and LLMs using modern MLOps practices, Kubernetes, distributed workflows, and large-scale scheduling.

Leads Uber’s global payment partnerships strategy and executive relationships with networks, acquirers, issuers, wallets, processors, and emerging payment platforms. Owns partner performance, commercial negotiations, market intelligence, strategic opportunities, and cross-functional payment integrations. Builds governance frameworks and leads a global team of partnership managers while representing Uber externally with industry partners and standards organizations.

Define scientific strategy and technical roadmaps for real-time rider pricing and incentive systems. Lead pricing algorithm architecture, experimentation standards, causal inference frameworks, and marketplace optimization at global scale. Translate statistical and economic insights into executive recommendations, guide junior scientists, and partner with Product and Engineering to productionize high-impact solutions.

Develop and deploy computer vision and multimodal models for document understanding, transcription, and verification. Responsibilities include model training, evaluation, production deployment, monitoring, scaling, edge optimization, and building robust evaluation frameworks. The role involves collaborating with infrastructure and product teams, analyzing production failures, and improving model accuracy, efficiency, privacy, and reliability across large-scale applications.