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SnapshotArtificial 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 RoleWe’re looking for a Research Engineer with exceptional programming and engineering skills, a deep understanding of large-scale neural network training and data processing, and a strong working knowledge of machine learning experimentation. The ideal candidate will have hands-on experience with training and developing multimodal models.
Key responsibilities:- Develop, maintain, and improve large-scale multimodal models, with a focus on video modeling, and the data pipelines for training and evaluation.
- Design and implement novel methods for evaluating and improving multi-modal generative models, particularly at the post-training stage.
- Build and maintain robust data pipelines for collecting and processing large-scale datasets, including human-labeled data.
- Collaborate with research scientists to translate research ideas into production-ready code.
In order to set you up for success as a Research Engineer at Google DeepMind, we look for the following skills and experience:
- BSc, MSc or PhD/DPhil degree in computer science, physics, mathematics, applied statistics, machine learning, or equivalent practical experience.
- Strong background in deep learning, with proven experience with relevant architectures (e.g., Transformers, GNNs, etc).
- Experience with training large-scale machine learning models, particularly in the multimodal domain (video, text).
- Excellent software engineering skills with a proven ability to build robust and scalable systems.
- Proficiency with ML and scientific libraries such as JAX, TensorFlow, PyTorch, NumPy, and Pandas.
- Experience with either large-scale data processing frameworks (e.g., Apache Beam, Spark) or distributed training infrastructure.
In addition, the following would be an advantage:
- Experience with LLMs, VLMs, and/or diffusion models training and inference.
- Experience with Computer Vision tasks (video classification, object tracking, depth estimation etc.)
- Experience working in a team on projects from the initial proof-of-concept stage to final implementation and evaluation.
