Staff Software Engineer, Capacity Engineering
Staff Software Engineer, Capacity Engineering
Job Description
Responsibilities
- 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
Qualifications
- 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
Locations
- San Francisco, CA, US; Remote, US (Remote)
Salary
177,185 - 364,795 USD / yearly
Estimated Salary Rangemedium confidence
220,000 - 350,000 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- GPU Architecturesintermediate
- Pytorchintermediate
- ML software stack (Scheduling, Data and Storage)intermediate
- Kubernetesintermediate
- Technical and performance engineeringintermediate
- Javaintermediate
- Pythonintermediate
- C++intermediate
- Communicating complex technical issuesintermediate
- ML Modelsintermediate
- Kernelsintermediate
- Optimization opportunitiesintermediate
- Large, cloud-native multi-tenant platformsintermediate
- AWS or similar cloud environmentsintermediate
- Infrastructure capacity and performanceintermediate
Required Qualifications
- Deep understanding of GPU Architectures, Pytorch, etc. (experience)
- Deep understanding of supporting parts of ML software stack like Scheduling, Data and Storage (experience)
- Hands on experience with shared platforms like Kubernetes (experience)
- Strong technical and performance engineering skills to collaborate with stakeholders on complex and ambiguous technical challenges (experience)
- Experience building and managing highly available distributed applications at scale (experience)
- Proficiency in software development languages such as Java, Python and C++ (experience)
- Excellent skills in communicating complex technical issues (experience)
- Understanding of ML Models, Kernels and optimization opportunities (experience)
- Hands-on experience with large, cloud-native multi-tenant platforms at Internet scale (experience)
- Experience with AWS or similar cloud environments (experience)
- Deep understanding of infrastructure capacity and performance (experience)
- Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent experience (experience)
Responsibilities
- 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
Tags & Categories
Staff Software Engineer, Capacity Engineering
Staff Software Engineer, Capacity Engineering
Job Description
Responsibilities
- 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
Qualifications
- 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
Locations
- San Francisco, CA, US; Remote, US (Remote)
Salary
177,185 - 364,795 USD / yearly
Estimated Salary Rangemedium confidence
220,000 - 350,000 USD / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- GPU Architecturesintermediate
- Pytorchintermediate
- ML software stack (Scheduling, Data and Storage)intermediate
- Kubernetesintermediate
- Technical and performance engineeringintermediate
- Javaintermediate
- Pythonintermediate
- C++intermediate
- Communicating complex technical issuesintermediate
- ML Modelsintermediate
- Kernelsintermediate
- Optimization opportunitiesintermediate
- Large, cloud-native multi-tenant platformsintermediate
- AWS or similar cloud environmentsintermediate
- Infrastructure capacity and performanceintermediate
Required Qualifications
- Deep understanding of GPU Architectures, Pytorch, etc. (experience)
- Deep understanding of supporting parts of ML software stack like Scheduling, Data and Storage (experience)
- Hands on experience with shared platforms like Kubernetes (experience)
- Strong technical and performance engineering skills to collaborate with stakeholders on complex and ambiguous technical challenges (experience)
- Experience building and managing highly available distributed applications at scale (experience)
- Proficiency in software development languages such as Java, Python and C++ (experience)
- Excellent skills in communicating complex technical issues (experience)
- Understanding of ML Models, Kernels and optimization opportunities (experience)
- Hands-on experience with large, cloud-native multi-tenant platforms at Internet scale (experience)
- Experience with AWS or similar cloud environments (experience)
- Deep understanding of infrastructure capacity and performance (experience)
- Bachelor’s degree in Computer Science, Engineering, or a related field, or equivalent experience (experience)
Responsibilities
- 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
Tags & Categories