Physical AI - Technical Program Manager

About the Team

Building a physical AI team from scratch means a lot of moving parts — engineering workstreams, lab readiness activities, hardware build cycles, test campaigns, and a set of dependencies that can cascade fast if they're not actively managed. That's where this role comes in. We need a TPM who is genuinely technical — someone who can sit in a hardware design review and understand what they're hearing, ask the right questions, and translate complex technical status into clear program-level decisions and risks. This isn't a coordination role dressed up as a TPM position. It's a role for someone who's comfortable operating in ambiguity, who knows how to build structure where none exists, and who can drive alignment across a team that's moving fast in multiple directions at once.

What You'll Be Doing

Program Planning & Execution

• Own the integrated program plan for the Boston physical AI team — covering hardware builds, lab operations, validation campaigns, and engineering milestones.
• Maintain and actively manage a living dependency map across onsite and remote engineering teams; identify risks before they become schedule impacts.
• Establish and drive program cadences — sprint planning, milestone reviews, risk reviews, and cross-team syncs — that keep the organization aligned without adding unnecessary overhead.
• Track open action items, decisions, and commitments across workstreams; own follow-through and hold teams accountable to their commitments.

Risk & Decision Management

• Proactively surface technical, schedule, and resource risks; develop and drive mitigation strategies rather than just flagging problems.
• Distinguish between decisions that need leadership visibility and those that can and should be resolved at the team level; create the right escalation pathways.
• Build and maintain program dashboards that give leadership accurate, real-time visibility into status, risks, and blockers — without requiring manual data collection from engineers.
• Facilitate tradeoff discussions when priorities conflict — bringing the right people into the room, structuring the decision, and ensuring clear outcomes are documented.

Cross-Team Alignment

• Serve as the connective tissue between onsite lab and engineering teams and broader organizational stakeholders — translating technical status into program language and vice versa.
• Own communication of program status, risks, and decisions to engineering leadership on a regular and ad-hoc basis.
• Coordinate with external teams — supply chain, facilities, IT, partner engineering organizations — to resolve dependencies that sit outside the immediate team boundary.
• Manage scope changes, new requests, and shifting priorities in a structured way that maintains clarity on what's in-plan and what's not.

Process & Tooling

• Build lightweight but effective program management processes appropriate for a fast-moving R&D environment — not enterprise overhead, but enough structure to prevent chaos.
• Own tooling decisions for program tracking (e.g., Jira, Confluence, Asana, or similar) and drive adoption across the team.
• Author clear RACI frameworks for key workstreams to eliminate ambiguity about who owns what.

Required Qualifications

• Bachelor's degree in Engineering, Computer Science, or a related technical field.
• 7+ years of technical program management experience, with at least 3 years managing programs that involve hardware development, physical systems, or robotics.
• Strong technical foundation — enough depth to understand hardware build processes, test and validation workflows, and engineering dependencies without needing every concept explained.
• Demonstrable track record of managing complex, multi-workstream programs in ambiguous or greenfield environments — not just running established processes.
• Excellent written and verbal communication; ability to write a sharp status update, a clean risk register, and a clear decision memo without much editing.
• Experience building program management infrastructure (processes, tools, cadences) from scratch.

Preferred Experience

• Experience in a physical AI, robotics, autonomous systems, or advanced hardware R&D organization.
• Exposure to hardware development lifecycles — NPI, prototype builds, DVT/EVT/PVT, or equivalent — and the specific dependencies and failure modes that come with them.
• Familiarity with agile methodologies adapted for hardware development environments.
• Prior experience at a hyperscaler, frontier AI lab, or high-growth technology company where ambiguity and speed are the norm, not the exception.

A Day in the Life

The morning starts with a quick scan of the program dashboard — two items from yesterday's hardware build session are still open, and one of them has an upstream dependency on a component delivery that's now pushed by a week. You update the risk register, draft a short note to the engineering lead flagging the impact on the integration timeline, and propose two mitigation options before you've finished your coffee. By midmorning you're running the weekly cross-team sync, walking through milestone status with the lab manager and tech lead, and facilitating a pointed conversation between the hardware and software teams about a shared interface spec that's been in limbo. You push for a decision before the meeting ends and make sure it's documented. In the afternoon you're heads-down in the program plan, updating dependencies in light of a scope change that came in yesterday, and drafting the agenda for next week's program review. You end the day responding to a few async questions from a partner team in a different timezone — the kind of thing that, if left unanswered, turns into a blocker three days later.