Lead Edge AI Engineer

Responsibilities

• Architect and prototype edge-AI pipelines — enabling local training, inference, and cross-device collaboration.
• Build developer-friendly APIs and SDKs that abstract distributed complexity into elegant, efficient experiences.
• Optimize performance across constrained and diverse hardware — GPUs, NPUs, and embedded accelerators.
• Integrate edge-first data flows with privacy-preserving and verifiable computation frameworks.
• Collaborate with product, research, and infrastructure teams to shape the developer experience for edge-native AI.
• Mentor engineers and help shape Source’s engineering culture around precision, performance, and trust.

Requirements

• Deep experience in AI/ML systems and model deployment in real-world edge environments.
• Strong proficiency in Rust, Go, C++, or Python.
• Familiarity with distributed systems, federated learning, or privacy-preserving AI.
• Understanding of edge compute hardware and runtime constraints.
• Previous experience in a startup or scale-up environment.
• Track record of delivering complex distributed or AI systems end-to-end.
• Curiosity for verifiable computing, zero-trust architectures, and data-centric AI design.
• A first-principles mindset — you care about building foundational systems that will last decades.