Summary

The team

Join Kraken’s Data org and own the Machine Learning platforms and systems used across the company. The team operates at company-wide scope, enabling multiple product and engineering teams to build, deploy, and operate ML models reliably in production. We work closely with product, engineering, data science, security, and compliance partners to ensure ML systems are scalable, observable, and aligned with Kraken’s business and regulatory requirements. Engineers on the team are expected to operate with high autonomy, handle broad problem spaces, and drive technical clarity in ambiguous environments. The team values deep technical expertise, strong ownership, and mentorship, with an emphasis on raising the bar across the organization.

The opportunity

1. Set long-term technical direction for Kraken’s AI/ML strategy, influencing multiple teams and initiatives.
2. Own the architecture and evolution of core AI/ML systems, including training, feature management, serving, experimentation, and monitoring.
3. Lead cross-team efforts to standardize ML development and operational practices across the company.
4. Drive the design and delivery of complex, high-impact ML systems for use cases such as fraud, risk, personalization, and recommendations.
5. Partner with senior engineering, product, and data leaders to identify where ML can unlock step-change improvements in scalability and efficiency.
6. Balance hands-on technical contributions with architectural leadership and design review responsibilities.
7. Evaluate emerging ML and GenAI technologies and lead their adoption when they provide durable platform-level value.
8. Mentor Staff and Senior engineers, shaping technical culture and raising the overall quality of ML engineering at Kraken.

Skills you should HODL

1. 10+ years of experience building and operating large-scale production Machine Learning systems.
2. Proven track record of owning ML platforms or infrastructure used by multiple teams or products.
3. Strong system design skills, with experience making long-term architectural tradeoffs in complex environments.
4. Expert-level proficiency in Python and strong experience with additional languages such as Scala, Go, or Rust.
5. Deep hands-on experience with ML frameworks (PyTorch, TensorFlow, scikit-learn) and model lifecycle management.
6. Extensive experience with MLOps practices, including experimentation frameworks, CI/CD, model monitoring, and reliability.
7. Strong background in data-intensive and distributed systems (Spark, object storage, large-scale batch and streaming).
8. Demonstrated ability to lead through influence, mentor senior engineers, and communicate technical strategy to diverse stakeholders.

Skills

• Communications Skills
• Development
• Machine Learning
• Operations
• Python
• Software Engineering
• Team Collaboration