The robotics industry is quietly raiding the autonomous vehicle talent pool, and for good reason: engineers who've shipped self-driving cars at scale are now the most sought-after hires in physical AI. Robotics startups and established companies are discovering that the skills required to deploy complex autonomous systems in the real world transfer directly to building home robots, industrial machines, and other physical AI applications. This talent migration reveals a critical insight about how emerging technology industries mature.

Why Are Robotics Companies Hunting for AV Veterans?

Adrian Macneil, cofounder and CEO of Foxglove, a data infrastructure platform serving robotics companies, explained the reasoning behind this hiring strategy. "We definitely look for AV experience," he said. "The reason is that AV was the first big application of physical AI, so people who have worked at AV companies have seen what it takes to ship complex physical AI at scale." At Foxglove, which employs 75 people, roughly 40% of the workforce came from autonomous vehicle or AV-adjacent companies, including Waymo, Motional, Aurora, Applied Intuition, Luminar, and Cruise.

The appeal is straightforward: autonomous vehicles represent a complete development cycle that robotics companies are now attempting to replicate. Building a self-driving car requires engineers to master the full pipeline from data collection through training, validation, and real-world deployment. That same pipeline applies to robots, whether they're designed for homes or factories.

"When it comes to hardware, there are just not that many exciting hardware companies that combine AI and cutting-edge technology," said Tony Zhao, CEO and cofounder of Sunday Robotics, which is building home robots.

Tony Zhao, CEO and cofounder of Sunday Robotics

At Sunday Robotics, between 30% to 50% of the roughly 70-person team has AV-related backgrounds, including experience at Tesla. Zhao himself worked on Tesla's Autopilot team, and his cofounder, Cheng Chi, was a software engineer at Nuro, which is preparing for a commercial robotaxi launch with Uber.

What Specific Skills Transfer From Autonomous Vehicles to Robotics?

The transferable skills span both software and hardware domains. From a software perspective, AV engineers understand "systems-level thinking," a critical capability in which engineers must ask fundamental questions about sensor selection, data quality, and how messy real-world inputs translate into reliable physical action. This differs sharply from working with large language models (LLMs), where inputs and outputs are purely textual and more straightforward to manage.

On the hardware side, AV experience is equally valuable. Few companies successfully combine cutting-edge AI with complex hardware systems, making AV veterans rare and highly valued. Additionally, autonomous vehicle engineers have worked in massive teams because validating and evaluating AV technology is an enormous operational undertaking. That experience managing large, complex projects translates directly to robotics development.

"Some of the best people to hire today to work on robotics are AV people, because it's very similar. The robotics field is starting to mirror some of the trends and maturity curve AVs went through from 2014 to the present," said Behrad Toghi, AI and Robotics lead at General Motors, who previously worked on Apple's Special Projects Group on autonomous cars.

Behrad Toghi, AI and Robotics lead at General Motors

Ritika Shrivastava, a former Tesla Autopilot engineer who cofounded Ember Robotics in 2024, captured the parallel nature of the work: "The mindset is exactly the same. In AVs, you have a lot of planning, sensing, and actuation, and then you have the same exact thing in robotics as well".

How to Identify Transferable Skills From AV to Robotics

• Data Pipeline Expertise: AV engineers have managed the complete cycle of collecting real-world sensor data, training models on that data, validating safety, and deploying systems at scale. Robotics companies need identical capabilities.
• Systems-Level Thinking: Understanding how to integrate sensors, compute, and actuation into a cohesive system that handles messy real-world conditions is a skill honed in autonomous vehicles and directly applicable to robots.
• Large-Team Coordination: Autonomous vehicle development requires coordinating across hardware, software, safety, and regulatory teams. Robotics startups scaling from prototypes to production need this organizational experience.
• Hardware-Software Integration: AV engineers understand the constraints of physical systems, sensor limitations, and how to design software that accounts for real-world hardware imperfections, a skill set rare in pure software backgrounds.
• Safety Validation and Testing: Deploying autonomous systems requires rigorous safety frameworks and testing protocols. Robotics companies building products for homes and factories need engineers who understand these methodologies.

Is This Talent Migration Accelerating?

The evidence suggests it is. In April 2026, Foxglove and venture capital firm Eclipse Capital hosted a "Physical AI Industry Night" in San Francisco's South of Market neighborhood, drawing over 200 people from more than 110 robotics companies. Nearly 30 of those companies came from the mobility sector, including Waymo, Tesla, Zoox, Nissan, and Toyota Research Institute.

Three of the four panelists at the event were robotics executives who graduated from the autonomous vehicle industry: Kevin Peterson, CTO of Bedrock Robotics, who came from Waymo; Ruijie (RJ) He, founding technical member of Mind Robotics, who was a director at Zoox; and Behrad Toghi from General Motors, who previously worked on Apple's autonomous vehicle efforts. Each panelist spoke about the direct transferability of AV experience to robotics development.

This migration reflects a broader pattern in technology: when a field matures enough to produce real-world deployments, the engineers who've shipped products become the most valuable hires in adjacent fields. Autonomous vehicles, despite their challenges and ongoing regulatory hurdles, have reached a maturity level where they're generating deployable systems. That experience is now the gold standard in physical AI hiring.

The robotics industry's aggressive recruitment of AV talent suggests that the path to scaling physical AI applications beyond self-driving cars runs directly through the expertise built over the past decade in autonomous vehicles. As robotics companies race to move beyond closed demonstrations and into real-world deployment, they're betting that the engineers who've already navigated that journey are their best bet for success.