Technical Product Manager

• Own end-to-end technical product execution for a product/experience stream from concept through launch and continuous improvement
• Translate outcome-based strategy into epics, user stories, acceptance criteria, and release-ready backlog
• Define and maintain scope, decision rights, and accountability boundaries across teams
• Lead cross-functional operating cadence across Product, Commercial, Systems Engineering, Architecture, Program Management, and R&D
• Partner with Engineering and Architecture on technical trade-offs balancing speed, quality, compliance, and business value
• Co-own release readiness with the Program Manager, including dependency management, risk escalation, and launch quality
• Embed quality and compliance guardrails through shift-left testing, clear controls, and traceable decisions
• Drive stream-aligned delivery metrics and OKRs to improve predictability, throughput, and customer impact
• Support reusable platform and shared experience-service adoption with Platform & Delivery teams
• Maintain documentation and communication of technical product decisions and rationale
• Orchestrate alignment across the Products & Experience operating model and coordinate with Technical Leads, Architects, and Program Managers

Director, Technical Product Owner

• Define product areas across Zoox's current vehicle and future platform
• Own successful product execution across its lifecycle
• Develop a deep understanding of the customer, market, and business objectives to identify opportunities and align the product domain with company goals
• Serve as Product Owner in a cross-functional product development process spanning hardware, firmware, software, and operations
• Prioritize features and ensure priorities are clearly communicated
• Define and lead a key pillar of the vehicle experience across currently deployed vehicles and future platforms
• Define vehicle and vehicle experiences that delight customers and enable scalable operations

Senior Technical Product Manager – Vehicle Experience

• Plan and coordinate execution of engineering initiatives for the Commercial Data Platform and Data Products.
• Create, plan, and execute software development plans while ensuring efficiency, scalability, and usability.
• Gather requirements and prioritize technical development with cross-functional teams and stakeholders.
• Lead discussions with key decision-makers and stakeholders to create a comprehensive data platform roadmap.
• Work closely with engineering using Agile methodologies, including Scrum, Kanban, and sprint planning.
• Define requirements and manage development processes, systems, and software infrastructure for data platforms and tools.
• Partner with laboratory and business stakeholders to elucidate requirements, optimize workflows, and lead data platform and product definition aligned with HIPAA and HITECH.
• Translate product requirements into Epics and Stories with clear user acceptance criteria.
• Track and drive development using an issue tracking system such as Jira.
• Facilitate interactions between users and engineers to ensure user needs are understood and met.
• Create and operationalize technical strategy and integrated roadmaps for data products, pipelines, and tooling.
• Align cross-functional milestones and deliverables across current and future programs.
• Provide a vision for future development focused on scalability and accessibility.
• Ensure data integrity throughout the pipeline using technical and process-oriented solutions.
• Report to the Associate Director, Technical Product Management.

Technical Product Manager III

• Research and define end-to-end Artificial Intelligence (AI) system requirements from capability to behavior to user impact
• Translate model capabilities, data constraints, and evaluation results into clear product and system decisions
• Make trade-offs across quality, latency, cost, reliability, and UX
• Work closely with Machine Learning (ML), backend, and mobile engineers on system design, evaluation, and iteration
• Define and evolve evaluation frameworks across offline metrics, online experiments, and human feedback
• Drive execution with clear specs, strong judgment, and disciplined prioritization
• Ensure systems ship quickly, safely, and reliably, with strong feedback loops
• Own product quality end-to-end, including correctness, predictability, and user trust
• Align AI capabilities with user needs and company priorities
• Establish clear roadmaps and priorities and enable fast iteration based on user feedback
• Keep teams aligned and focused on AI product goals

Technical Product Manager, Artificial Intelligence (AI) Systems

• Own the accuracy, reliability, and cost-efficiency of the AI behind CentrlX
• Build and own the evaluation foundation, including golden datasets, grading rubrics, LLM-as-judge pipelines calibrated against human labels, and regression suites
• Define measurable quality standards for document digitization, extraction, retrieval, groundedness, summaries, responses, evaluations, and multi-step agent runs
• Evaluate agent behavior, including tool selection, retrieval quality, step sequencing, and finished deliverable quality
• Convert client failures into permanent evaluation cases
• Benchmark models across providers on accuracy, latency, and cost
• Own model migrations and determine model deployment by workflow
• Track AI spend and optimize cost through routing, model tiering, caching, and context strategy
• Define logging and tracing requirements for prompts, retrieved context, outputs, tool calls, token counts, and latency
• Build in-app feedback capture with Product and Design
• Build, label, curate, and maintain evaluation datasets with holdouts and rotation
• Serve as the point of contact for AI quality escalations; triage, reproduce, identify root causes, and close the loop
• Write user stories and acceptance criteria and implement prompt, configuration, and model changes
• Publish regular quality and cost readouts
• Work with domain practitioners to encode industry judgment into evaluation rubrics

Technical Product Manager, Data Science

• Own and evolve the long-term product vision and technical strategy for a major product line, platform, or portfolio
• Architect and govern large-scale, cross-program EPICs and initiatives
• Identify systemic dependencies, technical risks, and organizational impacts across teams and business units
• Lead planning and execution of multi-program-increment initiatives
• Establish roadmap narratives connecting features to broader company strategy
• Partner with senior executives, architects, and cross-functional leaders on investments, priorities, and trade-offs
• Drive business process modeling and re-engineering using BPMN, value stream mapping, and swimlanes
• Establish standards for requirements elicitation, documentation, and traceability
• Define frameworks for feature definition and prioritization
• Design and govern success metrics covering adoption, performance, throughput, and value realization
• Report outcomes to senior leadership
• Mentor and coach senior and mid-level product managers
• Act as escalation point for complex, ambiguous, or high-risk product and technical challenges
• Represent the product organization in governance forums, architecture review boards, and executive planning sessions

Principal Technical Product Manager

• Define the resilience platform and own the product roadmap and delivery for specific platform features, including common telemetry interfaces, health-check contracts, attribution hooks, observability APIs, and joint deployment experience
• Translate developer pain points into features and integration partnerships
• Collaborate with the open-source developer community by prioritizing GitHub issues, gathering feedback, supporting contributors, and channeling community signals into the roadmap
• Collaborate with engineering on feature design, prioritization, execution, and architecture tradeoffs
• Align with Product, Engineering, Product Marketing, and Field teams on requirements, roadmaps, messaging, and engagements

Technical Product Manager – AI Infra Resilience

• Own the roadmap for shared platform services and data capabilities
• Understand platform needs across product teams and unblock high-value use cases
• Drive consistency without unnecessarily constraining product innovation
• Maintain understanding of domain models, datasets, services, APIs, and platform dependencies
• Prioritize and communicate trade-offs to engineering, product, and leadership
• Partner with engineering to build scalable APIs, services, pipelines, and interfaces
• Define canonical entities, relationships, contracts, events, permissions, and semantics across the data lifecycle
• Improve discovery, understanding, and consumption of shared capabilities
• Define standards for data quality, lineage, governance, discoverability, documentation, reliability, and developer experience
• Determine which capabilities belong at the platform layer versus individual products or verticals
• Evaluate AI and agent-based interactions with data and platform capabilities
• Define success metrics and measure adoption, reliability, reuse, and business impact

Staff Technical Product Manager – Platform

• Own the roadmap for shared platform services and data capabilities
• Work across product teams to understand platform needs, unblock high-value use cases, and drive consistency
• Maintain understanding of the domain model, datasets, services, APIs, and platform dependencies
• Prioritize and communicate trade-offs to engineering, product, and leadership
• Partner with engineering to build reliable, scalable APIs, services, pipelines, and interfaces
• Define canonical entities, relationships, contracts, events, permissions, and semantics across the data lifecycle
• Improve application teams’ ability to discover, understand, and consume shared capabilities
• Define standards for data quality, lineage, governance, discoverability, documentation, reliability, and developer experience
• Determine which capabilities belong at the platform layer versus individual products or verticals
• Evaluate how AI and agent-based systems should interact with company data and platform capabilities
• Define success metrics and measure adoption, reliability, reuse, and business impact
• Collaborate with product engineering, data engineering, data science, security, and product teams
• Turn fragmented systems and data into durable platform primitives for teams, applications, customers, and AI systems

• Own the roadmap for shared platform services and data capabilities
• Understand platform needs across product teams, unblock high-value use cases, and drive consistency without unnecessarily constraining innovation
• Maintain understanding of domain models, datasets, services, APIs, and platform dependencies
• Prioritize and communicate trade-offs to engineering, product, and leadership
• Partner with engineering to build reliable, scalable APIs, services, pipelines, and interfaces
• Define canonical entities, relationships, contracts, events, permissions, and semantics across the data lifecycle
• Improve discovery, understanding, and consumption of shared capabilities by application teams
• Define standards for data quality, lineage, governance, discoverability, documentation, reliability, and developer experience
• Determine which capabilities belong at the platform layer versus within individual products or verticals
• Evaluate AI and agent-based systems’ interaction with company data and platform capabilities
• Define success metrics and measure adoption, reliability, reuse, and business impact
• Collaborate with product engineering, data engineering, data science, security, and product teams