CTO – Physical AI scale up

Stealth Startup; Austin (TX); On-site; USD 230,000 - 270,000; Full time

Benefits offered by this job

Equity: 3-4%

OTE: $250K + post-Seed uplift

Job summary

Stealth Startup in Austin, TX is seeking a hands-on VP of Engineering (effectively CTO) to architect and build a category-defining physical AI architecture that operates directly on factory floors, driving real-world decisions in manufacturing, logistics, and energy.

You will lead a small, highly technical team with deep experience across industrial AI, robotics, and real-world systems, including ex-Palantir engineers and researchers from top institutions.

Responsibilities

• Architect and build our physical AI system—from edge devices (cameras, vibration/thermal sensors, PLCs via OPC-UA/Modbus) to cloud-enabling agents to operate directly on real-world machinery and processes
• Design real-time systems that ingest and interpret raw signals (video streams, time-series sensor data, PLC states) to understand machine behavior, line states, and failure modes
• Build the ontology that grounds agents in physical operations—machines, sequences, constraints, and state transitions across production and logistics systems
• Develop agentic control loops for quality, maintenance, and operations-detecting deviations, reasoning over root cause, and triggering actions (alerts, work orders, schedule adjustments)
• Stay hands-on in core systems: video pipelines, edge inference, streaming data systems, and low-latency processing across distributed environments
• Lead and scale a small, high-performance team while remaining the primary technical driver of architecture and execution
• Design systems that improve with deployment-capturing machine behaviors, failure signatures, and operational patterns across sites

Job description

We're offering

• $250K OTE
• 3-4% equity in a notable VC-backed company approaching Seed
• $100K+ post-Seed salary uplift
• Full ownership of a category-defining physical AI architecture

Must be Austin TX based or willing to relocate.

We're seeking

We are building multimodal physical AI agents that operate directly on the factory floor-reasoning over video, sensors, PLCs, and enterprise systems to drive real-world decisions in manufacturing, logistics, and energy.

Most AI today is built for language. It cannot understand machines, processes, or physical operations. Meanwhile, massive amounts of industrial intelligence remain trapped in sensors and systems.

We are redesigning the AI architecture for the physical world-and we're seeking a hands-on VP of Engineering (effectively our CTO) to build it with us.

Team you will join

You'll join a small, highly technical team of builders with deep experience across industrial AI, robotics, and real-world systems. The team includes ex-Palantir engineers, a former ABB robotics VP, and autonomous systems engineers from Audi and Volvo, alongside researchers from Harvard and MIT.

What you will do

• Architect and build our physical AI system-from edge devices (cameras, vibration/thermal sensors, PLCs via OPC-UA/Modbus) to cloud-enabling agents to operate directly on real-world machinery and processes
• Design real-time systems that ingest and interpret raw signals (video streams, time-series sensor data, PLC states) to understand machine behavior, line states, and failure modes
• Build the ontology that grounds agents in physical operations—machines, sequences, constraints, and state transitions across production and logistics systems
• Develop agentic control loops for quality, maintenance, and operations-detecting deviations, reasoning over root cause, and triggering actions (alerts, work orders, schedule adjustments)
• Stay hands-on in core systems: video pipelines, edge inference, streaming data systems, and low-latency processing across distributed environments
• Lead and scale a small, high-performance team while remaining the primary technical driver of architecture and execution
• Design systems that improve with deployment-capturing machine behaviors, failure signatures, and operational patterns across sites