AI Research Engineer

Gradient is building the world’s first fully AI runtime—a sovereign, peer-powered infrastructure where intelligence is hosted, served, and owned by the people. The age of Centralized Intelligence is ending. What comes next is open, agentic, and alive at the edge.

We’ve recently completed our $10M seed funding round, led by Pantera Capital and Multicoin Capital, with participation from HSG (formerly Sequoia Capital China) and other distinguished partners. This milestone is further supported by leading angel investors and advisors from spaces across AI, crypto, and beyond.

About the Role

We’re looking for a Research Engineer who’s passionate about shaping the future of AI infrastructure. In this role, you’ll work at the intersection of advanced machine learning models, distributed systems, and on‑chain coordination — helping to build a more open, equitable, and resilient intelligence layer for the world.

You’ll join a team of deep technologists exploring how foundation models and intelligent systems can be deployed, trained, and served in environments — while maintaining performance, privacy, and security.

Responsibilities

Design and implement protocols for AI training and inference (e.g. federated learning, distributed fine-tuning, secure model serving)

Build scalable systems to support AI workloads across heterogeneous networks and devices

Contribute to architectural decisions, performance optimization, and reliability engineering

Collaborate with researchers and system engineers to translate novel ideas into real-world systems

Stay current with the latest advancements in AI, distributed infrastructure, and cryptographic mechanisms

Requirements

Strong programming skills in Python, Rust, or Go

Experience with AI/ML model training and serving pipelines (e.g. PyTorch, JAX, TensorFlow)

Solid foundation in distributed systems, networking, or applied cryptography

Familiarity with concepts like federated learning, secure aggregation, or verifiable intelligence

Bonus: experience working with blockchain protocols, zkML, or decentralized networks

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