Sr. Research Engineer

Adobe
SeattlePosted 1 March 2026

Job Description

Our Company Changing the world through digital experiences is what Adobe’s all about. We give everyone—from emerging artists to global brands—everything they need to design and deliver exceptional digital experiences! We’re passionate about empowering people to create beautiful and powerful images, videos, and apps, and transform how companies interact with customers across every screen. We’re on a mission to hire the very best and are committed to creating exceptional employee experiences where everyone is respected and has access to equal opportunity. We realize that new ideas can come from everywhere in the organization, and we know the next big idea could be yours! The Opportunity The Research Engineering & Design Lab within Adobe Research is looking for a Sr. Research Engineer with a passion for the unique challenges involved in the development of high-performance ML models, with a particular focus on optimizing the resource consumption of large-scale generative models, including for cloud and on-device deployment. Join our efforts to turn cutting-edge research into new products and features in Adobe's web, desktop and mobile applications, and expand what's possible in tools for creative expression. What you'll Do At Adobe Research, you will work closely with both scientists and engineers to ensure that new technologies being developed for generative AI models, image processing, audio, video, animation, 3D and vector graphics will run efficiently in-cloud and on a variety of platforms. You'll help adapt models and algorithms to the limitations of different systems and devices. You will have the opportunity to: Play a key collaborative role in ambitious research projects Be valued as a specialist in your domain of expertise Build innovative tool that enables users to explore their creative potential Contribute to existing Adobe tools as well as completely new applications Impact products that are used by tens of millions of people Learn from your peers and grow into new opportunities What you need to succeed Passion for model optimization/compression and high-performance computing Solid deep learning skills, including practical experience in computer vision/natural language processing Knowledgeable about the current state of the art in ML efficiency Experience in improving the efficiency of mid- to large-scale ML models Software engineering expertise Proficiency with Python and ML libraries like PyTorch, TensorFlow, JAX, or similar Strong communication and collaboration skills Ph.D. /Master's degree in Computer Science or a related field, or 3 years of industry experience Additional Desirable Qualifications Knowledge of state-of-the-art machine learning methods for large scale multimodal models Experience on diffusion model optimization, neural network pruning/knowledge distillation/quantization/architecture search, sub-quadratic attention optimization, efficient architecture design and on-device ML Experience with sparse mixture of experts and related techniques Experience running ML models within a deployment environment (using TensorRT, AITemplate, CoreML, WinML, TensorFlow Lite, ONNXRuntime or similar) Hands-on experience in designing ML models between different platforms (Cloud, mobile, in-browser, etc.) Knowledge of design techniques for mobile-friendly ML models Proficiency with C++ Our compensation reflects the cost of labor across several  U.S. geographic markets, and we pay differently based on those defined markets. The U.S. pay range for this position is $164,600 -- $313,300 annually. Pay within this range varies by work location and may also depend on job-related knowledge, skills, and experience. Your recruiter can share more about the specific salary range for the job location during the hiring process. In Washington, the pay range for this position is $204,800 - $296,600 At Adobe, for sales roles starting salaries are expressed as total target compensation (TTC = base + commission), and short-term incentive ... (truncated, view full listing at source)
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