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Technical Leader for Large-Scale Model Systems
3 weeks ago
At the heart of our organization lies a mission to inspire creativity and enrich life. We're driven by a passion for innovation, and our products are designed to empower individuals to express themselves authentically.
We believe that diversity is the cornerstone of success, and we're committed to fostering an inclusive environment where every voice is valued.
Our team is built on a foundation of collaboration and mutual respect, with a shared goal of creating value for our communities and pushing the boundaries of what's possible.
- Work with a talented team of professionals who share your passions and values
- Contribute to the development of cutting-edge technologies that shape the future
- Enjoy a positive and supportive work environment that encourages growth and learning
- Positive team atmosphere
- Career growth opportunities
- Flat organizational structure
ResponsibilitiesWe're seeking a skilled professional to join our Applied Machine Learning - Enterprise team as a Site Reliability Engineer. In this role, you'll be responsible for ensuring the stability and performance of our large-scale model systems.
Your key responsibilities will include:
- Managing and overseeing the stability of both control and data aspects of large-scale model systems through effective DevOps practices
- Developing and enhancing observability systems for monitoring the stability of large-scale model systems
- Handling super large-scale cluster management and ensuring efficient operation and maintenance of large model systems
To succeed in this role, you'll need:
- A B.Sc or higher degree in Computer Science or a related field from an accredited institution
- A minimum of 5 years of R&D experience in cloud computing or large-scale model systems
- Proficiency in cloud-native technologies and understanding of the relevant technology stack
- Expertise in one of the following programming languages: Golang, Python, or Java
- Familiarity with cloud-native technologies for log collection, monitoring, and alerting
