The Role Design and operate production infrastructure for AI agent systems, including high-performance Rust services for model inference, orchestration, and execution. Build scalable distributed systems, MLOps capabilities, observability, guardrails, and failure recovery for millions of users. Optimize latency, throughput, reliability, and cost while partnering with agent systems and infrastructure teams to harden experimental prototypes for production. Summary Generated by Built In 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. 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. Stay connected Follow us on Twitter Learn on the Kraken Blog Connect on LinkedIn Candidate Privacy Notice Read Full Description Skills Required 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 across infrastructure and applied engineering teams High ownership mindset in a high-stakes production environment Experience building infrastructure for agent-based or LLM-powered systems Background in high-performance networking, asynchronous systems, or low-latency architectures Experience with container orchestration and cloud-native infrastructure Familiarity with evaluation frameworks and model performance monitoring at scale Experience in fast-moving 0-to-1 or platform-building teams Kraken Digital Asset Exchange Compensation & Benefits Highlights The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Kraken Digital Asset Exchange and has not been reviewed or approved by Kraken Digital Asset Exchange. About This Summary Fair & Transparent Compensation — Pay is considered competitive for many roles, with strong total compensation reported for in‑demand technical and senior positions. Market‑aligned packages are highlighted across key functions, indicating the ability to reach top‑of‑market for certain hires. Equity Value & Accessibility — Equity grants are available for most roles, complemented by bonus programs and the option to receive a portion of pay in crypto. This ownership‑oriented design is positioned as part of the standard total compensation approach. Flexible Benefits — A remote‑first operating model, flexible time off, and a remote‑workstation setup bonus emphasize autonomy and location flexibility. Core benefits are framed to support distributed work across multiple regions. Learn more about Kraken Digital Asset Exchange's Compensation & Benefits → Kraken Digital Asset Exchange Insights What's It Like to Work at Kraken Digital Asset Exchange? 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