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 The Lead Technical Program Manager - Platform Integration owns program-level execution across Waabi's platform programs, with a focus on cross-functional integration between hardware development, onboard software, embedded systems, autonomy, safety, and operations. You are the connective tissue between teams that operate on fundamentally different cadences: embedded software with kanban-based delivery, autonomy research with probabilistic timelines, and safety validation with methodical gate-based requirements. You will surface where those cadences collide, identify gaps in requirements and execution, map the critical path accurately, and ensure risks are visible and owned so we maintain velocity and deliver on time. You Will.. • Define and maintain integrated platform program plans covering key milestones, cross-team dependencies, and ensure on-time delivery. • Ensure all stakeholder inputs are captured and integrated into program plans before design commitments lock in. • Build and enforce dependency maps across Hardware, Software, Systems and Safety, and Ops, identify the critical path, and ensure every dependency has an owner and an ETA. • Own reporting into executive forums. Surface risks early with data-backed Path to Green options, not just status updates. • Track system VnV and overall test campaigns, ensuring test coverage, sign-off readiness, and milestone alignment across teams. • Build cross-functional roadmaps that give hardware, software, and autonomy a shared line of sight on interdependencies and sequencing. • Champion tools to capture the single source of truth for program work, ensuring consistency across programs. • Build scalable tracking mechanisms and systematic processes that work at fleet scale. Technical breadth. Ask the right questions, spot unrealistic assumptions in a plan, and know when a dependency is a real blocker versus a process gap. You can engage with engineers across hardware, embedded software, autonomy, and safety without needing to be a domain expert in any one area. Self-starter who builds processes from scratch. Build processes and iterate based on what actually works and impose structure without creating overhead. Cross-functional influencer. Work on highly cross functional programs spanning embedded software, onboard software, hardware, autonomy, safety, and operations. Risk-first mindset. Surface problems early, escalate without hesitation, and always arrive with options. You can dive deep and bring clarity to status updates and a Path to Green. Comfort with ambiguity. Thrive in bringing clarity and structure to ambiguity and convert ambitious goals into programs with hard success criteria, DRIs, and trackable milestones. Required Qualifications: • 6+ years of technical program management experience, with a strong focus on cross-functional hardware, software and systems integration programs. • Demonstrated experience working across teams with meaningfully different development cadences (e.g., robotics, autonomous vehicles, industrial automation, IoT, or defense systems). • Proven ability to build cross-functional program structure from scratch: dependency maps, risk registries, executive reporting, and milestone tracking. • Strong written and verbal communication. You can write a crisp one-pager for an executive audience and facilitate a working-level dependency review with engineers in the same week. • Experience in autonomous vehicles, robotics, or advanced driver-assistance systems (ADAS). • Hands-on experience with program tracking tools like Jira, Asana and Microsoft Project. • Demonstrated use of AI tools to improve program visibility, reduce manual overhead, or accelerate decision-making. Preferred Qualifications: • Familiarity with embedded systems and software development and the constraints that come with it. • Familiarity with ML development workflows and an understanding of how model iteration cadences interact with hardware and software delivery timelines. Experience working alongside ML or AI research teams in a program capacity is a plus. • Experience establishing or scaling a triage process for field issues across engineering teams. • Background in systems engineering, computer engineering, electrical engineering, or a related technical discipline.