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Snapshot
We are seeking a software engineer to define, drive, and critically contribute to the next generation of the state-of-the-art ML models on TPU. As part of the Pre-Training team you will co-design the model, and implement critical components across Model architecture, ML frameworks, custom kernels and platform, to deliver frontier models with maximum efficiency.
About Us
Artificial Intelligence could be one of humanity’s most useful inventions. At Google DeepMind, we’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence. We use our technologies for widespread public benefit and scientific discovery, and collaborate with others on critical challenges, ensuring safety and ethics are the highest priority.
The Role
We’re looking for a Software Engineer to re-define efficient training of frontier LLMs at massive scale. This role offers an opportunity to influence the design of frontier LLM models, and drive an effort to ensure efficient training and inference.
Key responsibilities:
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Being responsible for Pre-Training efficiency and optimising the performance of the latest models on Google’s fleet of hardware accelerators - throughout the entire LLM research, training and deployment lifecycle.
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Being responsible for guiding model design to ensure inference-efficiency.
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Greatly improving the performance of LLM models on hardware accelerators by optimizing at all levels, including developing custom kernels when necessary.
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Collaborating with the compiler, framework, and platform teams. And ensure efficient training at industry-largest scale.
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Profile models to identify performance bottlenecks and opportunities for optimization.
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Develop low-level custom kernels for maximum performance of the most critical operators.
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Collaborating with research teams by enabling new critical operators in advance of their availability in frameworks and compilers.
About You
You're an engineer looking to re-define efficient training of frontier LLMs at massive scale and have:
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A proven track record of critical contributions to the distributed training of LLMs at 1e25 FLOPs scale on modern GPU/TPU clusters
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Experience in programming hardware accelerators GPU/TPUs via ML frameworks (e.g. JAX, PyTorch) and low-level programming models (e.g. CUDA, OpenCL)
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Experience in leveraging custom kernels and compiler infrastructure to improve performance on hardware
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Experience with Python and neural network training (publications, open-source projects, relevant work experience, etc.)
The US base salary range for this full-time position is between $235,000 - $350,000 + bonus + equity + benefits. Your recruiter can share more about the specific salary range for your targeted location during the hiring process.
Application deadline: March 12, 2025
Note: In the event your application is successful and an offer of employment is made to you, any offer of employment will be conditional on the results of a background check, performed by a third party acting on our behalf. For more information on how we handle your data, please see our Applicant and Candidate Privacy Policyopen_in_new.
Top Skills
What We Do
We’re a team of scientists, engineers, machine learning experts and more, working together to advance the state of the art in artificial intelligence. We use our technologies for widespread public benefit and scientific discovery, and collaborate with others on critical challenges, ensuring safety and ethics are the highest priority.
Our long term aim is to solve intelligence, developing more general and capable problem-solving systems, known as artificial general intelligence (AGI).
Guided by safety and ethics, this invention could help society find answers to some of the world’s most pressing and fundamental scientific challenges.
We have a track record of breakthroughs in fundamental AI research, published in journals like Nature, Science, and more.Our programs have learned to diagnose eye diseases as effectively as the world’s top doctors, to save 30% of the energy used to keep data centres cool, and to predict the complex 3D shapes of proteins - which could one day transform how drugs are invented.
