Build and scale training libraries and infrastructure for autonomous driving models, enabling training on thousands of GPUs and massive datasets.
Compensation
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are: 224,000 USD – 356,500 USD for Level 5; and 272,000 USD – 431,250 USD for Level 6.
Responsibilities
Crafting, scaling, and hardening deep learning infrastructure libraries and frameworks for training on multi-thousand GPU clusters.
Improving efficiency throughout the training stack: data loaders, distributed training, scheduling, and performance monitoring.
Building robust training pipelines and libraries to handle massive video datasets and enable rapid experimentation.
Collaborating with researchers, model engineers, and internal platform teams to enhance efficiency, minimize stalls, and improve training availability.
Owning core infrastructure components such as orchestration libraries, distributed training frameworks, and fault-resilient training systems.
Build and scale deep learning infrastructure libraries and frameworks for autonomous driving models.
Compensation
Your base salary will be determined based on your location, experience, and other objective job-related factors. The expected base salary ranges for this position are: 224,000 USD – 356,500 USD for Level 5; and 272,000 USD – 431,250 USD for Level 6.
Responsibilities
Crafting, scaling, and hardening deep learning infrastructure libraries and frameworks for training on multi-thousand GPU clusters.
Improving efficiency throughout the training stack: data loaders, distributed training, scheduling, and performance monitoring.
Building robust training pipelines and libraries to handle massive video datasets and enable rapid experimentation.
Collaborating with researchers, model engineers, and internal platform teams to enhance efficiency, minimize stalls, and improve training availability.
Owning core infrastructure components such as orchestration libraries, distributed training frameworks, and fault-resilient training systems.
Responsible for all technical support related aspects at a specific customer: escalations, fleet performance, system installations, improvement programs and upgrades.
Responsibilities
Responsible for all technical support related aspects at a specific customer: escalations, fleet performance, system installations, improvement programs and upgrades.
Acts as intermediary and field escalation focal point between customer and Division by providing technical support and documentation to field engineers, technicians and product support personnel who are diagnosing, troubleshooting, repairing and debugging equipment.
Provides Alpha Site support to Engineering specifically related to installation, operation, calibration, service and/or testing of a new hardware, process or software design in house or on an engineering tool
Provides Beta Site Support; specifically related to installation, operation, calibration, service and/or testing of a new hardware, software or processes on a customer tool.
Initiates and provides review of ECO's to ensure data supports fix and follow up with communication to the field.
Global Technical Trainer delivering technical training and support for Applied Materials Taiwan GTLC, including classroom, lab, and customer site instruction, with domestic and international travel.
Responsibilities
Presents technical training in house and at customer sites
Manages classroom/lab activities
Provides technical expertise and troubleshooting skills
Principal-level PyTorch training framework expert to help drive performance, scalability, and correctness of large-scale AI training on AMD Instinct™ accelerators.
Locations (3)
San Jose, California, United States
Seattle, Washington, United States
Austin, Texas, United States
Responsibilities
Act as a technical authority for PyTorch training at AMD, setting direction for performance, scalability, and reliability
Drive optimization of key PyTorch training workloads (LLMs/foundation models) across single-node and multi-node systems
Improve and debug training performance in areas such as DDP/FSDP, gradient checkpointing, mixed precision, memory planning, and communication/computation overlap
Partner with ROCm compiler/runtime, kernel, and driver teams to resolve performance bottlenecks and correctness issues across the full stack
Principal Software Engineer – PyTorch Training FrameworksAdvanced Micro Devices, Inc · San Jose, US+2 more