Research Engineer

Adobe
San JosePosted 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 Research Engineer passionate about the unique challenges involved in evaluating and improving Large Language Model (LLM) and Vision Language Model (VLM), and helping design and build next-generation multimodal intelligence systems. 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 research scientists and engineers at the intersection of computer vision, natural language processing, and generative modeling. You will help create the best media creation and editing user experience using the latest technologies. 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. Contribute to research that can be applied to Adobe product development. Help integrate novel research work into Adobe’s product. Lead and collaborate on research projects across different Adobe divisions Your responsibilities will include: Large dataset collection and data quality improvements utilizing state-of-the-art VLMs. Develop pipelines and evaluation tools to measure model performance on multimodal tasks. Help define metrics and evaluation rubrics for generative models. Use VLM-based evaluators to judge content generation and editing results, including realism, faithfulness to prompts, spatial consistency, semantic correctness, and safety. Iteratively refine and improve VLM judges for specific tasks (e.g., generative model evaluation, multimodal reasoning, editing correctness). Work closely with research scientists, engineers, and product teams to support model development. What You Need to Succeed M.S. degree in Machine Learning, AI, Computer Science, or related field (or equivalent practical experience). Strong programming skills in Python and experience with common ML frameworks. Solid deep learning skills, including practical experience in computer vision and natural language processing. Strong communication and collaboration skills. Experience working with large datasets. Familiarity with modern VLMs and LLMs. Additional Desirable Qualifications 3+ years of working experience in the related field. Experience with multimodal model evaluation, data annotation pipelines, or generative model assessment. Knowledge of prompt engineering and model fine-tuning. Knowledge of state-of-the-art machine learning methods for large-scale diffusion and language models. Proficiency with web development. 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 $120,700 -- $238,600 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 California, the pay range for this position is $164,800 - $238,600 At Adobe, for sales roles starting salaries are expressed as total target compensation (TTC = base + commission), and shor ... (truncated, view full listing at source)
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