Adobe is revolutionizing digital experiences through their innovative Firefly family of creative generative AI models. As a Senior ML Engineer for Platform & Data, you'll be at the forefront of evolving these models that reach millions of creatives worldwide. The role focuses on building and optimizing the infrastructure that powers Adobe's AI initiatives, particularly the Firefly platform.
You'll be working with cutting-edge technology, designing and implementing robust AI/ML infrastructure solutions using Kubernetes and Python on AWS cloud. The position requires expertise in distributed training frameworks and GPU optimization, where you'll be responsible for improving system performance, resiliency, and scalability.
The ideal candidate brings a strong technical foundation with a PhD or Master's in computer science and 5+ years of relevant industry experience. You should be proficient in Python and have deep knowledge of ML infrastructure, including model serving, training, and resource management. Experience with distributed PyTorch and strong problem-solving abilities are essential.
This is a unique opportunity to impact creative professionals globally by helping reinvent how they work through AI. You'll be joining Adobe's mission to change the world through digital experiences, working with a team passionate about pushing the boundaries of what's possible with generative AI.
The role offers competitive compensation ranging from $182,900 to $334,500 annually, along with equity opportunities and comprehensive benefits. You'll be working with Adobe's proprietary dataset of hundreds of millions of licensed images, developing commercially safe AI models that will shape the future of creative work.
As part of the Firefly team, you'll collaborate closely with ML researchers and engineers to accelerate the training of cutting-edge models, while also staying current with the latest innovations in academia and the open-source community. This position combines technical depth with the opportunity to drive innovation in infrastructure practices supporting pioneering machine learning research and development.