Software Engineer, Large Scale Pre-Training Performance

As a Software Engineer at Google DeepMind, your primary responsibilities will include optimizing the performance of large-scale pre-training models on TPU. You will lead initiatives to improve efficiency throughout the entire lifecycle of LLM training and deployment, guide model design to ensure inference-efficiency, and collaborate with multiple teams to troubleshoot and enhance performance across Google’s fleet of hardware accelerators.

To excel as a Software Engineer at Google DeepMind, you should have a proven track record in distributed training of LLMs on GPU/TPU clusters, experience programming hardware accelerators via ML frameworks like JAX or PyTorch, and familiarity with low-level programming models such as CUDA or OpenCL. Additionally, a strong background in neural network training and Python is essential.

In the Software Engineer role at Google DeepMind, you will play an influential part in the design of advanced LLM models. By optimizing model performance on TPUs and guiding the development of model architecture, you will not only improve current systems but also help pave the way for future innovations in AI.

As a Software Engineer working on frontier LLM models at Google DeepMind, your work will directly affect the efficiency and effectiveness of machine learning processes at scale. By enhancing performance through optimizations and collaborating with leading experts, you'll contribute significantly to advancements in AI technology with public benefit in mind.

The US base salary range for the full-time Software Engineer position at Google DeepMind is between $235,000 to $350,000, along with bonuses, equity, and benefits. Your recruiter can provide more specifics regarding the salary range based on your targeted location during the hiring process.

In your response, highlight specific projects where you managed distributed training, including the tools you used, the scale of the models, and key challenges you overcame.

Discuss your approach towards profiling models, identifying bottlenecks, and the techniques you have successfully implemented to enhance TPU performance, showcasing your technical depth.

Provide detailed strategies you've implemented in previous projects focusing on how you prioritize inference efficiency in architecture and when collaborating with team members.

Explain any specific instances where you've developed custom kernels, the challenges faced, and the enhancements they brought to model performance.

Share insights about the teamwork dynamics, communication strategies, and how collaborative efforts led to significant improvements in model training or performance.

Convey your commitment to continual learning through reading relevant literature, attending conferences, participating in forums, or engaging with the open-source community.

Present a clear example of how low-level programming models impacted your projects, emphasizing technical decisions and their outcomes.

Outline your systematic methodology to profile models, identify issues, and the tools you use to measure performance against target metrics.

Discuss potential challenges such as resource allocation, data management, and model convergence, along with your strategies to mitigate such issues.

Articulate your passion for artificial intelligence, your alignment with Google DeepMind's mission, and how the role will help you achieve your career aspirations while contributing to the field.