Senior Software Engineer – Streaming
Sentry
AWSCloudDistributed SystemsGoGoogle Cloud PlatformJavaKafkaKubernetesPythonRustSparkSQL
Staff Software Engineer, Capacity Engineering
full-time
Posted on:
Location Type: Hybrid
Location: San Francisco • California • 🇺🇸 United States
Visit company websiteSalary
💰 $170,371 - $350,763 per year
Job Level
Lead
Tech Stack
AWSCloudJavaKubernetesPythonPyTorch
About the role
- Manage the ML hardware capacity that powers the models running at Pinterest
- Improve the efficiency of ML Infrastructure at Pinterest
- Build develop and mature profiling and optimization capabilities for ML Infrastructure at Pinterest scale
- Collaborate with ML Platform, Infrastructure Engineering and SRE teams in their mission to deliver highly available, resilient, secure and efficient ML foundations for Pinterest’s tech stack
Requirements
- Deep understanding of GPU Architectures, Pytorch, etc.
- Deep understanding of supporting parts of ML software stack like Scheduling, Data and Storage
- Hands on experience with shared platforms like Kubernetes
- Strong technical and performance engineering skills to collaborate with stakeholders on complex and ambiguous technical challenges
- Experience building and managing highly available distributed applications at scale
- Proficiency in software development languages such as Java, Python and C++
- Excellent skills in communicating complex technical issues
- Understanding of ML Models, Kernels and optimization opportunities
- Hands-on experience with large, cloud-native multi-tenant platforms at Internet scale
- Experience with AWS or similar cloud environments
- Deep understanding of infrastructure capacity and performance
- Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent experience.
Benefits
- Information regarding the culture at Pinterest and benefits available for this position can be found here.
Applicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard skills
GPU ArchitecturesPytorchKubernetesJavaPythonC++ML ModelsKernelscloud-native platformsdistributed applications
Soft skills
technical engineering skillscollaborationcommunication
