About the job
This position is for a Software Engineer that will lead the development of machine learning tools to run, optimize, and analyze machine learning workloads. This candidate must have had experience leading machine learning tool projects, preferably starting from architecture through several generations of delivery to customers. Deep knowledge of profiling and optimization, resource management, scheduling, code generation are needed. The ideal candidate will have worked on new instruction set architectures, which may include CPU, NPU, GPU and other forms of compute.
Responsibilities
Lead the design and implementation of ML infrastructure platform, building systems for capacity management, workload scheduling, and fleet orchestration across ML accelerators.
Work with ML scientists, training infrastructure engineers, hardware teams, and internal customers to ensure the ML Infra service delivers seamless ML Accelerator access with low wait times, high utilization, and zero-config deployment from various environments.
Create metrics, implement automation and other improvements, and resolve the root cause of software defects.
Build high-impact solutions to deliver to our large customer base.
Participate in design discussions, code review, and communicate with internal and external stakeholders.
Work cross-functionally to help drive business decisions with your technical input.
Qualifications
Minimum
3+ years of non-internship professional software development experience
2+ years of non-internship design or architecture (design patterns, reliability and scaling) of new and existing systems experience
Experience programming with at least one software programming language
Preferred
3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience
Bachelor's degree in computer science or equivalent
Experience taking a leading role in building complex software or computing infrastructure that has been successfully delivered to customers
Experience with AWS Services including EC2, Lambda, S3, DynamoDB, SQS
Experience in Kubernetes, Docker or containers ecosystem, or experience managing full application stacks from the OS up through custom applications and experience in any Bigdata architecture
Experience with version control systems and CI/CD pipeline implementation
Strong proficiency in Go/Java, Python, and Javascript/Typescript
Application and kernel performance profiling and optimization
Proficiency in integrated software/hardware performance analysis and optimization
Experience designing and operating production services