Staff Research Engineer, Applied AI, DeepMind

Minimum qualifications:

• Bachelor's degree in Electrical Engineering, Computer Engineering, Computer Science, or a related field, or equivalent practical experience.
• 8 years of software development experience, including system design, data structures, and algorithms.
• 5 years of experience building, training, and deploying machine learning models into production environments.

Preferred qualifications:

• Experience working with Generative AI/LLMs, specifically in model fine-tuning, custom dataset curation, and designing evaluation pipelines.
• Proven experience architecting, prototyping, and shipping complex ML systems from the ground up, with a deep understanding of machine learning and deep learning (e.g., Transformers, Diffusion, LLMs).
• Good communication skills with a proven ability to engage with cross-functional teams, and take ambiguous, open-ended real-world problems and translate them into clear technical designs and product scope.

About the job:

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.

We are seeking a Staff Research Engineer, Applied AI to lead the development and deployment of novel applications, leveraging Google’s generative AI models. In this role, you will rapidly develop new features, and work across partner teams to deliver solutions, and maximize impact for Google and top customers. You will be instrumental in translating AI research into real-world products, and demonstrating the capabilities of latest-generation models. You will build and ship AI-powered software, ideally with experience in early-stage environments where you have contributed to scaling products from initial concept to production. You will be motivated by the opportunity to drive product and business impact.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.Individual pay is determined by factors including job-related skills, experience, and relevant education or training.

US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits

Learn more about benefits at Google.

Responsibilities:

• Partner closely with multiple external parties and internal cross-functional teams to navigate ambiguity, deeply understand real-world issues, and define clear product objectives and technical designs.
• Drive the curation of specialized datasets, design evaluations across industry verticals, and execute model fine-tuning to achieve optimal real-world performance.
• Lead the engineering and development of novel solutions from 0 to 1, utilizing internal platforms and tools to build sophisticated agents and workflows powered by DeepMind foundation models.
• Synthesize and upstream learnings from third-party partners to our core research teams, by sharing real-world evaluations, edge cases, and deployment signals, which can inform the development of future frontier models.
• Act as a technical leader in the applied AI space, setting best practices for generative AI deployment and demonstrating the peak capabilities of frontier models in solving high-impact problems end-to-end.