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What the job involves
The main requirements, responsibilities and hiring steps.
Requirements
- Strong engineering background in systems AI infrastructure GPU/runtime work networking SDKs ML inference developer tools or hardware-adjacent software
- Excellent coding ability in one or more of Linux kernel wireless driver CUDA or similar systems/product engineering stacks
- Comfort debugging across application runtime model serving network OS hardware and developer experience layers
- Strong product instinct for builder trust usability and shipping
- Evidence of serious technical work through shipped products research systems open-source projects startup engineering infra work hardware/software prototypes or demos with real users
- Genuine interest in local AI personal computing low-latency interaction privacy-preserving systems and nearby compute
- Comfort working across time zones
Nice to have
- Early-stage mindset
- Product instinct
- Builder empathy
- Cross-functional collaboration
- Technical curiosity
Day to day
- Build the local compute layer for personal AI with runtime SDK GPU offload and developer tools
- Design and implement low-latency AI paths for voice agents multimodal assistants local copilots smart-space intelligence and robotics interfaces
- Own complex technical problems end to end including architecture implementation debugging profiling documentation and launch
- Work closely with hardware product and research teams to turn wireless GPU architecture into developer-facing experiences
Hiring process
- Drop us a message
- Apply directly