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Software Engineer, ML Accelerators

DeepMind Mountain View, CA

Mountain View, CA

Today

$174k - $252k/yr Full time Ai Accelerators Hardware Acceleration Hardware Software Co Design

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Job Description

DeepMind is building a next-generation compute platform with a strong emphasis on ML hardware acceleration and software and hardware co-design. In this onsite role in Mountain View, you will work on the ML compiler stack and inference infrastructure that connects AI workloads to low-level hardware operators, with a focus on performance tuning and practical debugging.

This full-stack Software (SW) position sits at the intersection of compiler technology and high-performance inference. You will design and develop infrastructure that supports efficient execution, while partnering on SW/HW codesign, kernel optimization, and ongoing performance improvements.

What you will do

Own end-to-end software development for the ML compiler and inference infrastructure.
Design and build an ML compiler stack that bridges AI workloads with low-level hardware operators.
Create software infrastructure that enables high performance inference.
Drive SW/HW co-design activities, including kernel optimization, performance tuning, and debugging.

What you bring

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

Preferred qualifications

PhD degree in Computer Engineering, Computer Science, or a related field.
2 years of experience in full-stack system development for hardware accelerators.
2 years of experience in collaborative, hyper-scalar software development and engineering best practices.
Experience scoping, planning, and executing projects toward team goals in ambiguous environments.

Technologies

Python
C++
JAX
PyTorch
XLA

Location: Mountain View, CA (onsite)
Compensation: USD 174,000 - 252,000 per year

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