Rust Systems Engineer — AI Infrastructure (Production)

Kraken; United States; Remote; USD 150,000 - 190,000; Full time

Job summary

Kraken is building the AI Infrastructure team to power agent systems at scale. You will design the infrastructure layer and develop fast Rust services for model inference, orchestration, and execution in production.

You will work on scalable systems handling millions of users, implement ML deployment patterns, and optimize performance and cost across end-to-end pipelines. This is a high-responsibility role in a fast-moving crypto platform.

Qualifications

• 5+ years of experience building and operating high-scale production systems.
• Strong proficiency in Rust and systems-level programming.
• Experience with ML infrastructure, model serving, or MLOps in production.
• Experience designing observability, monitoring, and failure recovery systems.

Responsibilities

• Design and build the infrastructure layer powering AI agent systems in production.
• Develop high-performance Rust services for model inference, orchestration, and execution.
• Architect scalable systems capable of supporting millions of users and high throughput.
• Build reliable ML infrastructure and MLOps patterns for deployment and monitoring.
• Define guardrails, observability, and failure handling for agent-driven workflows.
• Optimize latency, throughput, and cost across inference and orchestration layers.
• Partner with the Agent Systems team to harden production systems.
• Contribute to foundational infrastructure decisions in a high-scale environment.

Skills

Rust

Distributed systems

Performance optimization

Observability

MLOps in production

Ownership mindset

High-scale architectures

Tools

Kubernetes

Cloud-native infrastructure

Container orchestration

Job description

Kraken is building the AI Infrastructure team to power agent systems at scale. You will design the infrastructure layer and develop fast Rust services for model inference, orchestration, and execution in production.

You will work on scalable systems handling millions of users, implement ML deployment patterns, and optimize performance and cost across end-to-end pipelines. This is a high-responsibility role in a fast-moving crypto platform.