Lead Edge AI Engineer

Source Inc.; Tulsa (OK); On-site; USD 200,000 - 300,000; Full time

Job summary

A cutting-edge tech company seeks a Lead Edge AI Engineer to revolutionize AI deployment across edge environments. This role involves architecting edge-AI pipelines, building user-friendly APIs, and optimizing performance on diverse hardware. Candidates should possess strong AI/ML systems experience and proficiency in languages like Rust or Python. Join our innovative team to shape the future of decentralized, verifiable AI infrastructure.

Qualifications

• Deep experience in AI/ML systems and model deployment in real-world edge environments.
• Strong proficiency in programming languages such as Rust, Go, C++, or Python.
• Familiarity with distributed systems, federated learning, or privacy-preserving AI.

Responsibilities

• Architect and prototype edge-AI pipelines enabling local training and inference.
• Build developer-friendly APIs that abstract distributed complexity.
• Optimize performance across diverse hardware including GPUs and NPUs.

Skills

AI/ML systems experience

Proficiency in Rust, Go, C++, or Python

Familiarity with distributed systems

Understanding of edge compute hardware

Startup or scale-up experience

Curiosity for verifiable computing

Job description

We build Source and we build with Source. We maintain an open-source, edge-native data stack and deploy it in production where the cloud isn't an option — phones, laptops, vehicles, robots, ground stations, satellites. Engineer-led, no roadmap theater, no process for the sake of process. The plan is to ship, learn, and fix what breaks. Upstream, we're making the stack faster, more correct, and less annoying to use. Downstream, we're deploying real workloads for partners on hardware that doesn't forgive lazy engineering. We run managed services too, for teams that want edge infra without the ops burden. If you'd rather debug a CRDT merge on a satellite than sit through a sprint retro, you'll like it here.

Location: SF/Bay Area + NA/EU

Salary: $200,000 - $300,000 + equity

Why we are hiring this role

At Source, we’re building the foundational data infrastructure for an edge-first world — a world where intelligence lives not in distant clouds but across billions of devices, vehicles, robots, and satellites.

AI is breaking free from the data center. The future of intelligence depends on compute that happens where data is created — instantly, privately, and verifiably. Yet today, the edge is fragmented. Developers are forced to trade off between performance and convenience, privacy and usability, autonomy and control.

We’re changing that.

Source is redefining how data is managed, shared, and computed across distributed environments — enabling AI systems to train, adapt, and collaborate directly at the edge. Our edge-first data management stack makes it possible to build a new generation of AI that is:

• Edge-first and privacy-preserving — data is processed, verified, and shared directly at the edge.
• Verifiable and trustworthy — every interaction can be proven, not just assumed.
• Collaborative by design — intelligence that learns across devices and environments without centralized control.

The result: AI that’s faster, safer, and more resilient — the foundation of truly distributed intelligence.

Why this role matters

As Lead Edge AI Engineer, you will own Source’s edge-AI engineering roadmap and make developing at the edge as natural and powerful as building in the cloud.

You’ll design the systems that let developers deploy, orchestrate, and verify AI models across edge environments — from federated learning and on-device inference to adaptive compute pipelines running on heterogeneous hardware.

This role sits at the intersection of distributed systems, AI infrastructure, and edge computing — bringing together model execution, verifiable computation, and developer experience. You’ll help define the standards for how AI operates in decentralized, privacy-preserving networks.

Working closely with our research, product, and infrastructure teams, you’ll directly impact the company’s technical trajectory and define what edge-first AI looks like in practice.

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 for the role

• 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.

Why Join Source

This is a rare opportunity to build the foundation for edge-native AI at one of the most innovative companies in distributed computing.

As Lead Edge AI Engineer, you’ll help shape how intelligence operates across billions of edge devices — from chips to constellations. You’ll join a small, world-class team defining the next generation of verifiable, decentralized AI infrastructure.

At Source, you’ll work on deep infrastructure that makes edge-first intelligence possible — systems that bring verifiability, privacy, and autonomy to the next wave of AI. If you’ve ever wanted to build the data layer that will unlock the edge-first future of AI, this is that moment.