Research Engineer, Pretraining, DeepMind at DeepMind

Job description

At Google, research-focused Software Engineers are embedded throughout the company, allowing them to setup large-scale tests and deploy promising ideas quickly and broadly. Ideas may come from internal projects as well as from collaborations with research programs at partner universities and technical institutes all over the world. From creating experiments and prototyping implementations to designing new architectures, engineers work on real-world problems including artificial intelligence, data mining, natural language processing, hardware and software performance analysis, improving compilers for mobile platforms, as well as core search and much more. But you stay connected to your research roots as an active contributor to the wider research community by partnering with universities and publishing papers. Artificial intelligence will be one of humanity’s most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority. We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort. Individual pay is determined by factors including job-related skills, experience, and relevant education or training. US: $174000 - $253000 (USD) + 15% bonus target + equity + benefits Learn more about benefits at Google . • Focus on inference-optimized model design for Gemini pretraining. • Understand common Accelerated Linear Algebra (XLA) primitives and how JAX code runs on TPUs in practice. • Work across the Large Language Model (LLM) preparation stack (e.g., pretraining, fine tuning, serving) to deliver strong models. • Bachelor's degree in Computer Science, Mathematics, Applied Stats, Machine Learning, a related field, or equivalent practical experience. • 5 years of experience with inference-optimized model design. • Experience working with product teams. Minimum qualifications: • Bachelor's degree in Computer Science, Mathematics, Applied Stats, Machine Learning, a related field, or equivalent practical experience. • 5 years of experience with inference-optimized model design. • Experience working with product teams. Preferred qualifications: • Experience conducting applied research to improve the quality and training/serving efficiency of large transformer-based models. • Experience with fine-tuning large models (e.g., supervised, RLHF).

Requirements

Must-haves

We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
Learn more about benefits at Google .
Focus on inference-optimized model design for Gemini pretraining.
Understand common Accelerated Linear Algebra (XLA) primitives and how JAX code runs on TPUs in practice.
Work across the Large Language Model (LLM) preparation stack (e.g., pretraining, fine tuning, serving) to deliver strong models.
Bachelor's degree in Computer Science, Mathematics, Applied Stats, Machine Learning, a related field, or equivalent practical experience.
5 years of experience with inference-optimized model design.
Experience working with product teams.
Bachelor's degree in Computer Science, Mathematics, Applied Stats, Machine Learning, a related field, or equivalent practical experience.
5 years of experience with inference-optimized model design.
Experience working with product teams.

Nice-to-haves

US: $174000 - $253000 (USD) + 15% bonus target + equity + benefits
Experience conducting applied research to improve the quality and training/serving efficiency of large transformer-based models.
Experience with fine-tuning large models (e.g., supervised, RLHF).

Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

aillmmachine learning

Platform: google
Effort: medium