Senior Product Designer, Autonomy Data & Tooling

Waabi, founded by AI visionary Raquel Urtasun, is the leader in Physical AI. With a world-class team, we're unlocking the next era of autonomous transportation with technology that's powering commercial autonomous trucks and robotaxis. Waabi is backed by and partners with world leaders in AI, automotive, logistics, and deep tech.   With offices in Toronto, San Francisco, Dallas, and Pittsburgh, Waabi is growing quickly and looking for diverse, innovative and collaborative candidates who want to impact the world in a positive way. To learn more visit: www.waabi.ai We're seeking a Senior Product Designer to own the human-in-the-loop workflows at the core of how Waabi turns raw driving data into a safer autonomy stack: the software environments where expert human judgment and automation work as one to make every engineer, analyst, operator, and researcher faster.   These are the workflows where a human expert and an automated system have to move together: data labeling, map annotation, track testing, and release testing. Your job is to design the environments that make that collaboration fast and high-signal, and then bring shared patterns into the design system for compounding returns across every internal and partner-facing surface.   You'll join as the third product designer at Waabi, sitting on the Product, Design & TPM team. You'll be the systems-minded builder who turns one-off UI work into compounding returns across the whole platform.   You will… Design data labeling and annotation workflows. You'll design the tools for data curation, internal lightweight labeling, QA, and the feedback loops that connect label quality to model performance. Today this work lives in many surfaces, and your goal will be to unify the workflows to accelerate training data throughput and quality, to continually improve autonomy safety at scale. Design data discovery surfaces and annotation tools. Grow the primary tools for finding, filtering, and curating AV data across logs, events, simulations, and labels. You'll help design the experience that lets triage analysts, systems engineers, autonomy engineers, and ML researchers navigate petabytes of driving and simulated data. You'll also own the design for our map annotation tooling: task management, branch/merge workflows, annotator feedback loops, defect logging, and urban surface street annotation as Waabi expands from highway trucking to urban robotaxi deployments. Ground the work in research. Get out of the tools and into the workflows. Shadow labelers, annotators, triage analysts, and right-seaters in their real environment. Produce qualitative and quantitative insight on where time and signal are lost, and disseminate it so the whole team designs from the same picture of the user. Build and scale Waabi's design system. Work with designers and full-stack engineers to take our nascent system and turn it into a robust, branded component system. This means complex, reusable components: timeline scrubbers, rich multi-select filters, pass/fail summary cards, map visualization primitives, data tables with inline actions. These components serve triage, metrics dashboards, data exploration, mapping, fleet ops, and partner-facing UI. Establish front-end and design infrastructure for tooling self-service. Build the context architecture that lets teams build their own UI with UX best practices baked in. Enable robust prototyping with read-only or snapshot data so teams can iterate without touching production. Ship directly in code. You'll contribute to production React codebases using AI-assisted tools. The shipped product is the deliverable, not a Figma handoff.   Qualifications Data-centric workflow designer who builds in code. 5-8+ years designing complex, data-rich tools where the primary interface is structured data: tables, filters, search, dashboards, maps, or annotation tools. You see the cross-tool workflow as your unit of work, not the single screen. You can make dense information scannable and actionable without dumbing it down, and you validate by building working prototypes on real data, not static mocks. Design-system builder. You think in components, tokens, patterns, and infrastructure, and see the reusable primitive inside every one-off request. You've built or significantly grown a design system, and you understand the difference between a component library and a design system: tokens, documentation, governance, adoption, and the political work of supporting engineers to use it. AI-native builder. You use AI-assisted coding (Cursor, Claude Code, or a similar agentic setup) as a default part of how you prototype and ship, not a novelty. It's how you get from idea to working software fast. First-principles thinker. You strip problems down to their fundamentals. The tools you'll design have no off-the-shelf pattern to copy. Curation workflows, map annotation, track test playlist management, data lineage visualization: these require you to understand the domain deeply and invent the right interaction model. Strong front-end intuition. You don't need to be a senior React engineer, but you understand component architecture, state management, and the constraints of web-based data visualization well enough to design things that are buildable and performant. Infrastructure mindset. You're energized by the meta-problem: how do we make everyone faster? You see AGENTS.md files, context documentation, and component APIs as design deliverables, not overhead. Bonus / Nice to Have Advanced AI-assisted workflows. Custom agentic setups, and real opinions about how AI changes the relationship between design systems and self-service UI. GIS or mapping tools background. Experience with map annotation, geospatial data, or tools like Mapbox, QGIS, or proprietary mapping tools. Data platform or ML tooling experience. Prior work designing labeling tools, data curation interfaces, dataset management, or ML experiment tracking. Design system at scale. You've maintained a system used by 10+ engineers across multiple product surfaces and dealt with the versioning, adoption, and governance challenges that come with it. AV, robotics, or safety-critical domains. Prior experience where the quality of tooling directly impacts physical safety.