$63,000 – $74,0006h ago
Kraken
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.
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 Agent Systems team is a 0→1 engineering group building internal AI-powered agents that interact directly with company systems. The mandate is to dramatically increase internal speed of execution through automation, intelligent inference, and workflow orchestration.
This team operates at the intersection of AI, backend systems, and applied product engineering. The work is pragmatic and fast-moving — prototypes are expected, but the bar for production reliability remains high. Engineers on this team move fluidly between experimentation and shipping, building systems that reason over user interactions and take meaningful action across internal tools.
You’ll work closely with cross-functional partners across product, infrastructure, and internal operations to deploy agent-driven capabilities that compound leverage across the organization.
Own and evolve streaming data pipelines that power live inference and real-time model serving across Kraken's AI infrastructure
Design and build feature stores that serve low-latency, high-reliability features to production ML models
Implement and maintain streaming systems using RisingWave, Apache Flink, or Kafka Streams, selecting the right tool for the workload
Partner with ML engineers and AI infra teams to define data contracts, feature schemas, and pipeline SLAs
Drive pipelines toward real-time where batch exists today reducing latency from hours to seconds
Ensure data quality, observability, and auditability across all streaming and feature engineering systems
Contribute to inference pipeline tooling where data engineering and model serving intersect
Evaluate emerging streaming and feature store technologies and shape the team's technical roadmap
5+ years in data engineering with at least 2 years focused on streaming systems in production
Hands-on experience with RisingWave, Apache Flink, Kafka Streams, or comparable stream processing frameworks
Strong understanding of feature store design — online/offline consistency, point-in-time correctness, low-latency serving
Experience building data pipelines that feed production ML models or inference systems
Proficiency in Python and/or Scala; SQL fluency required
Familiarity with data quality frameworks, pipeline observability, and SLA ownership
Comfortable operating in a fast-moving, ambiguous environment embedded within an AI-focused team
Direct experience with RisingWave in production
Exposure to inference pipeline architecture or model serving infrastructure
Experience with feature platforms
Crypto or fintech domain experience
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.
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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