Senior Software Engineer – AI Infrastructure

Kraken; Greater London; On-site; GBP 90,000 - 140,000; Full time

Job summary

Kraken’s AI Infrastructure team builds production systems powering intelligent agents at scale, focusing on model inference, orchestration, and execution. The role demands Rust expertise and strong systems thinking to deliver reliable, high-performance services across millions of users.

Joining the team means shaping foundational infra in a high-stakes environment, with emphasis on observability, latency optimization, and cross-functional collaboration with infrastructure and applied engineering

Qualifications

• 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.

Responsibilities

• 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 with the Agent Systems team to translate prototypes into production systems.
• Contribute to foundational infrastructure decisions in a high-scale environment.

Skills

Rust

Distributed systems

Reliability engineering

Performance optimization

Observability

High-scale production

Tools

Container orchestration

Cloud-native infrastructure

Model serving / MLOps

Job description

Building the Future of Open Finance

Payward - the parent company behind Kraken, NinjaTrader, Breakout, xStocks, Payward Services and CF Benchmarks - has spent the last 15 years building one of the most modern and globally accessible financial infrastructure platforms in the industry, built to advance an open, global financial system.

Building the Future of Open Finance

Payward - the parent company behind Kraken, NinjaTrader, Breakout, xStocks, Payward Services and CF Benchmarks - has spent the last 15 years building one of the most modern and globally accessible financial infrastructure platforms in the industry, built to advance an open, global financial system.

Before you apply, we encourage you to explore our culture page to understand what drives us and how we work.

The team

Founded in 2011, Kraken is one of the world's longest-standing crypto platforms, trusted by over 10 million individuals and institutions across the globe. It offers spot trading, margin, futures, staking, and OTC services, with products built for both individual investors and institutional clients.

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

Unless a specific application deadline is stated in the job posting, applications are accepted on an ongoing basis.

Please note, applicants are permitted to redact or remove information on their resume that identifies age, date of birth, or dates of attendance at or graduation from an educational institution.

We consider qualified applicants with criminal histories for employment on our team, assessing candidates in a manner consistent with the requirements of the San Francisco Fair Chance Ordinance.

Our commitment

Payward is powered by people from around the world and we celebrate the diverse talents, backgrounds, contributions, and unique perspectives that everyone brings to the table. We hire based on merit, seeking out people with the right abilities, knowledge, and skills for the job. We encourage you to apply for roles where you don't fully meet the listed requirements, especially if you're passionate or knowledgeable about crypto.

We may ask candidates to complete job-related skills or work-style assessments as part of our hiring process. These assessments evaluate competencies relevant to the role and are applied consistently across candidates for similar positions. Results are considered alongside experience and interviews, and are not the sole basis for any employment decision.

As an equal opportunity employer, we don't tolerate discrimination or harassment of any kind, whether based on race, ethnicity, age, gender identity, citizenship, religion, sexual orientation, disability, pregnancy, veteran status, or any other protected characteristic as outlined by federal, state, or local laws.

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