Posted by employer 15 hours ago
First seen on Joblaze 6 months ago
Last verified on the company career page 3 hours ago
Skills & Technologies
Requirements
- Experience
- 5+ years
Not disclosed in this posting: compensation, visa sponsorship.
Benefits
Joblaze summary
In this role, the Senior Software Engineer for AI Infrastructure at Kraken focuses on designing and building the foundational systems that support AI agents in a high-performance production environment. Key skills include proficiency in Rust, a strong grasp of distributed systems, and experience with ML infrastructure and MLOps. This position is ideal for seasoned engineers with a background in high-scale production systems who thrive in fast-paced, mission-driven settings. The team operates at the core of Kraken's innovative crypto solutions, emphasizing reliability and performance.
Joblaze insights
- Listed about 6 months ago — first seen on Joblaze April 16, 2026. Last confirmed on Kraken's careers page October 15, 2026.
- Rust appears in 4.8% of 62 comparable senior ai/ml roles in United Kingdom; Observability appears in 1.6% of 62 comparable senior ai/ml roles in United Kingdom.
Quick facts
- Is the Senior Software Engineer – AI Infrastructure role remote?
- Yes — Kraken lists this as a fully remote position.
- How much experience is required?
- At least 5 years of relevant experience for this Senior Software Engineer – AI Infrastructure role.
- What's the tech stack?
- Joblaze extracted these technologies from the posting: ML, MLOps, Observability, Rust, distributed systems, failure recovery.
- What seniority level is this role?
- Kraken targets senior candidates for this position.
- Is this full-time or contract?
- Full-time for this Senior Software Engineer – AI Infrastructure role at Kraken.
From the original posting
The AI Infrastructure team builds and operates the production systems that power intelligent agents at scale. This team sits at the foundation of the agent platform, ensuring that model inference, orchestration, and execution layers are reliable, observable, and performant under real-world load.
Working closely with the Agent Systems team and broader infrastructure partners, this group owns the core primitives that enable agents to safely operate across internal systems. The environment is high-scale and high-stakes — systems serve millions of users and must meet strict reliability, latency, and correctness standards.
This is a deeply production-oriented team. Engineers here combine strong systems thinking with applied ML infrastructure experience, building in Rust and operating services where performance and failure modes matter.
The opportunity
Design and build the infrastructure layer powering AI agent systems in production
Develop high-performance Rust services that handle model inference, orchestration, and execution
Architect scalable systems capable of supporting millions of users and high request throughput
Build reliable ML infrastructure and MLOps patterns for model deployment, evaluation, and monitoring
Define guardrails, observability, and failure handling for agent-driven workflows
Optimize latency, throughput, and cost across inference and orchestration layers
Partner closely with the Agent Systems team to translate experimental prototypes into hardened production systems
Contribute to foundational infrastructure decisions in a high-scale, high-impact environment
What You Bring
5+ years of experience building and operating high-scale production systems
Strong proficiency in Rust and systems-level programming
Deep understanding of distributed systems, reliability engineering, and performance optimization
Experience operating services serving millions of users or high-throughput workloads
Familiarity with ML infrastructure, model serving, or MLOps in production environments
Experience designing observability, monitoring, and failure recovery systems
Strong collaboration skills working across infrastructure and applied engineering teams
High ownership mindset in high-stakes production environment
Nice to haves
Experience building infrastructure for agent-based or LLM-powered systems
Background in high-performance networking, async systems, or low-latency architectures
Experience with container orchestration and cloud-native infrastructure
Familiarity with evaluation frameworks and model performance monitoring at scale
Experience working in fast-moving 0→1 or platform-building teams
Stay connected
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