Research Engineer, Human Understanding, DeepMind

Research and implement novel models and other multimodal techniques for a more holistic understanding of human likeness across visual, audio, and textual data.Conduct experimental research cycles from hypothesis to deployment, focusing on areas like scalable deepfake detection, privacy-preserving matching, and consistent human likeness generation.Take ownership of substantial technical projects within human likeness modeling (HLM), from ideation and design to implementation and evaluation, often involving cross-functional collaboration.Inform and contribute to the development of scalable and efficient research infrastructure for HLM models and datasets.Design and execute strategies for tuning and adapting vision-language models (VLMs) and other foundation models for specific HLM tasks, such as improved explainability and nuanced likeness measurement.Minimum qualifications:Bachelor’s degree in Computer Science, Machine Learning, a related technical field, or equivalent practical experience.5 years of experience developing machine learning models (e.g., audio/speech-visual models).Experience working with and tuning vision language models.Experience programming in Python and with deep learning frameworks (e.g., JAX, Flax, or Gemax).Experience conducting research and development, including experimental design, implementation, and analysis.Preferred qualifications:Experience with Generative AI techniques and architectures.Experience with multimodal learning, integrating information from different data types (e.g., vision, audio, text).Familiarity with Reinforcement Learning or alignment methods.Understanding of privacy-preserving machine learning or responsible AI practices.A track record of publications in AI/ML conferences (e.g., NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV).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 + benefitsLearn more about benefits at Google.Applicants in the County of Los Angeles: Qualified applications with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act.Note: By applying to this position you will have an opportunity to share your preferred working location from the following: Mountain View, CA, USA; Los Angeles, CA, USA.Bachelor’s degree in Computer Science, Machine Learning, a related technical field, or equivalent practical experience.5 years of experience developing machine learning models (e.g., audio/speech-visual models).Experience working with and tuning vision language models.Experience programming in Python and with deep learning frameworks (e.g., JAX, Flax, or Gemax).Experience conducting research and development, including experimental design, implementation, and analysis.