[Remote] Senior Infrastructure Software Engineer, AI/ML Platform
Note: The job is a remote job and is open to candidates in USA. Dropbox is seeking a Senior Software Engineer to join their Machine Learning platform team. In this role, you will architect and develop software infrastructure that enables customers to build impactful machine learning solutions, while collaborating with engineers and data scientists to enhance the ML development process.
Responsibilities
- Build infrastructure capable of managing metadata for hundreds of billions of files, handling hundreds of petabytes of user data, and facilitating millions of concurrent connections.
- Lead the expansion of Dropbox's function as the data-fabric, connecting hundreds of millions of applications, devices, and services globally, while also driving initiatives to enhance interoperability and adaptability across diverse ecosystems.
- Measure and optimize Dropbox's analytics platform to maintain its status as one of the most advanced in the industry for extracting meaningful insights from vast data volumes.
- Collaborate with cross-functional teams to innovate and implement solutions that enhance the performance, reliability, and security of Dropbox's infrastructure, ensuring a seamless experience for users worldwide.
- Mentor and guide junior team members, sharing knowledge and best practices to cultivate a culture of continuous learning and professional growth within the infrastructure engineering team.
- Stay current with emerging technologies and industry trends to continuously enhance Dropbox's infrastructure and maintain a competitive edge in the market.
Skills
- 8+ years of professional software development experience
- Extensive experience building and owning large-scale, multi-threaded, geographically distributed backend systems
- Experience with ML infrastructure
- Highly skilled at developing and debugging in C/C++, Java, or Go, with knowledge of Python a plus
- Strong communication skills and ability to work effectively in a collaborative team environment
- Familiarity with relevant technology stacks (ie. AWS, Kubernetes, Docker, Kubeflow, Ray, Tensorflow, PyTorch)
Education Requirements
- BS, MS, or PhD in Computer Science or related technical field involving coding (e.g., physics or mathematics), or equivalent technical experience
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