O pozici
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.
Proof of 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.
Co budeš dělat
- 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
Koho hledáme
- 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
Benefity
- 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.
