Key skills
Machine Learning systemsPythonMLOps practicesSystem designML frameworksData-intensive systemsMentorshipTechnical communicationGoRustScalaAIMachine LearningMLGenAIPyTorchTensorFlowscikit-learnMLOpsSparkCI/CDLeadership
About this role
Kraken is a mission-focused company rooted in crypto values, aiming to accelerate the global adoption of crypto. The Sr. Staff AI/ML Engineer will own the architecture and evolution of core AI/ML systems, lead cross-team efforts to standardize ML practices, and mentor engineers while driving the technical direction of Kraken's AI/ML strategy.
Responsibilities:
- Set long-term technical direction for Kraken’s AI/ML strategy, influencing multiple teams and initiatives
- Own the architecture and evolution of core AI/ML systems, including training, feature management, serving, experimentation, and monitoring
- Lead cross-team efforts to standardize ML development and operational practices across the company
- Drive the design and delivery of complex, high-impact ML systems for use cases such as fraud, risk, personalization, and recommendations
- Partner with senior engineering, product, and data leaders to identify where ML can unlock step-change improvements in scalability and efficiency
- Balance hands-on technical contributions with architectural leadership and design review responsibilities
- Evaluate emerging ML and GenAI technologies and lead their adoption when they provide durable platform-level value
- Mentor Staff and Senior engineers, shaping technical culture and raising the overall quality of ML engineering at Kraken
Requirements:
- 10+ years of experience building and operating large-scale production Machine Learning systems
- Proven track record of owning ML platforms or infrastructure used by multiple teams or products
- Strong system design skills, with experience making long-term architectural tradeoffs in complex environments
- Expert-level proficiency in Python and strong experience with additional languages such as Scala, Go, or Rust
- Deep hands-on experience with ML frameworks (PyTorch, TensorFlow, scikit-learn) and model lifecycle management
- Extensive experience with MLOps practices, including experimentation frameworks, CI/CD, model monitoring, and reliability
- Strong background in data-intensive and distributed systems (Spark, object storage, large-scale batch and streaming)
- Demonstrated ability to lead through influence, mentor senior engineers, and communicate technical strategy to diverse stakeholders