Software Engineer – ML Accelerators & Inference Equity

Google LLC; Mountain View (CA); Presencial; USD 174.000 - 252.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Equity

Benefits

Descripción de la vacante

Google DeepMind is seeking engineers for a role focusing on ML accelerators, compiler stacks, and high-performance inference infrastructure. You will contribute to bridging AI workloads with hardware operators, while actively participating in SW/HW codesign and optimization efforts across our compute platform.

The position emphasizes initiative and cross-disciplinary collaboration, with impact on AI inference workloads and product-ready systems for Google’s scale.

Formación

• Bachelor's degree in Computer Science, a related technical field, or equivalent practical experience.
• Experience in software development using Python and C++.
• Experience with ML hardware accelerators, ML compiler stacks (e.g., JAX, PyTorch, XLA), and kernel development.

Responsabilidades

• Direct full-stack Software role focusing on ML compiler and inference infrastructure.
• Design and develop ML compiler stack to bridge AI workloads and low-level Hardware operators.
• Design and develop SW infrastructure to enable high performance inference.
• Drive SW/HW codesign, kernel optimization, performance tuning, and debugging.

Conocimientos

Python & C++ programming

ML accelerators & compiler stacks

Educación

Bachelor's degree in Computer Science or related field

PhD degree in Computer Engineering/Computer Science (preferred)

Descripción del empleo

Google DeepMind is seeking engineers for a role focusing on ML accelerators, compiler stacks, and high-performance inference infrastructure. You will contribute to bridging AI workloads with hardware operators, while actively participating in SW/HW codesign and optimization efforts across our compute platform.

The position emphasizes initiative and cross-disciplinary collaboration, with impact on AI inference workloads and product-ready systems for Google’s scale.