We are seeking a talented Research Scientist with a strong background in generative modeling, particularly diffusion models, to join our modeling team. This role is ideal for candidates with deep expertise in diffusion models applied to images, videos, or 3D assets and scenes.
\nWhile experience in one or more of the following areas is a strong plus: large-scale model training, research in 3D computer vision.
\nYou will collaborate closely with researchers, engineers, and product teams to bring advanced 3D modeling and machine learning techniques into real-world applications, ensuring that our technology remains at the forefront of visual innovation. This role involves significant hands-on research and engineering work, driving projects from conceptualization through to production deployment.
\nKey responsibilities include designing, implementing, and training large-scale diffusion models for generating 3D worlds, developing and experimenting with large-scale diffusion models to add novel control signals, adapting to target aesthetic preferences, or distilling for efficient inference, collaborating closely with research and product teams to understand and translate product requirements into effective technical roadmaps, contributing hands-on to all stages of model development including data curation, experimentation, evaluation, and deployment, continuously exploring and integrating cutting-edge research in diffusion and generative AI more broadly, acting as a key technical resource within the team, mentoring colleagues, and driving best practices in generative modeling and ML engineering.
\nIdeal candidate profile includes 3+ years of experience in generative modeling or applied ML roles, extensive experience with machine learning frameworks such as PyTorch or TensorFlow, especially in the context of diffusion models and other generative models, deep expertise in at least one area of generative modeling, strong history of publications or open-source contributions involving large-scale diffusion models, strong coding proficiency in Python and experience with GPU-accelerated computing, ability to engage effectively with researchers and cross-functional teams, clearly translating complex technical ideas into actionable tasks and outcomes, comfortable operating within a dynamic startup environment with high levels of ambiguity, ownership, and innovation.
\nNice to have includes contributions to open-source projects in the fields of computer vision, graphics, or ML, familiarity with large-scale training infrastructure, experience integrating machine learning models into production environments, led or been involved with the development or training of large-scale, state-of-the-art generative models.
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\nOur products will put artists and songwriters first, through four key principles:
\nAs a Research Scientist, you will conduct groundbreaking research in music generation, run large-scale experiments, create practical applications, and collaborate with a cross-functional team to craft innovative solutions to complex challenges.
\nYou will have a direct impact on Spotify’s products, tools, and services, working on projects that influence the entire organization. You will also engage with the broader research community by publishing your findings, delivering talks, and attending top conferences.
\nWe offer a flexible work arrangement, allowing you to work where you work best, as long as you are within the EMEA region and operate within the Central European and GMT time zone for collaboration.
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\nOur products will put artists and songwriters first, through four key principles:
\nPartnerships with record labels, distributors, and music publishers: We’ll develop new products for artists and fans through upfront agreements, not by asking for forgiveness later.
\nChoice in participation: We recognize there’s a wide range of views on use of generative music tools within the artistic community. Therefore, artists and rightsholders will choose if and how to participate to ensure the use of AI tools aligns with the values of the people behind the music.
\nFair compensation and new revenue: We will build products that create wholly new revenue streams for rightsholders, artists, and songwriters, ensuring they are properly compensated for uses of their work and transparently credited for their contributions.
\nArtist-fan connection: AI tools we develop will not replace human artistry. They will give artists new ways to be creative and connect with fans.
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\nYou will have a direct impact on Spotify’s products, tools, and services, working on projects that influence the entire organization. You will also engage with the broader research community by publishing your findings, delivering talks, and attending top conferences.
\nWe offer you the flexibility to work where you work best! For this role, you can be within the North Americas region as long as we have a work location. This team operates within the Eastern Standard time zone for collaboration.
\nCore working hours are CET 3pm-6pm / EST 9am-12pm.
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\nThe role of the Research Engineer will be to develop state-of-the-art methods for multimodal generative AI models, with a primary focus on image generation and editing. This role is for the team behind “Nano Banana”.
\nAs a Research Engineer at Google DeepMind, you will lead our efforts in developing novel tools, infrastructure, and algorithms towards the end goal of solving and building Artificial General Intelligence. You will work collaboratively within and across Research fields, drawing on expertise from a variety of disciplines including deep learning, computer vision, language modeling, and advanced generative architectures.
\nKey responsibilities include designing, implementing, and evaluating cutting-edge deep learning algorithms, data curation, and evaluation infrastructure for multimodal generative AI, with a particular emphasis on image synthesis. You will report and present research findings and developments clearly and efficiently both internally and externally, verbally and in writing. You will also suggest and engage in team collaborations to meet ambitious research goals, while also driving significant individual contributions.
\nTo succeed as a Research Engineer at Google DeepMind, we look for the following skills and experience:
\nIn addition, the following would be an advantage:
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