Senior Research Engineer, Generative Prototyping

📍 Job Overview

Job Title: Senior Research Engineer, Generative Prototyping

Company: Adobe

Location: Seattle, Washington, United States

Job Type: Full-Time

Category: Engineering / Research & Development (Focus on Generative AI & Creative Tools)

Date Posted: May 08, 2026

Experience Level: 5-10 Years

Remote Status: On-site

🚀 Role Summary

• Lead the invention, prototyping, and iteration of user-facing Generative AI (GenAI) video authoring experiences with production-level rigor.

• Architect and integrate heterogeneous models (in-house and third-party) across text, image, audio, and video into cohesive, agentic workflows for creative professionals.

• Build and refine multimodal editing and conversational interfaces, directly informing the technical direction and product roadmap for emerging AI-first products and Creative Cloud.

• Collaborate closely with filmmakers and creative professionals in tight feedback loops, translating ambiguous creative needs into working systems and shaping strategic direction through user studies.

📝 Enhancement Note: This role is deeply embedded in Adobe's strategic push into Generative AI for creative workflows, specifically focusing on video authoring. The "Senior Research Engineer" title, combined with "Generative Prototyping" and "agentic workflows," indicates a need for a highly skilled individual capable of both cutting-edge research and practical, production-ready engineering. The emphasis on "production-level rigor" and "shipping transformative tools" suggests a strong bias towards applied development and demonstrable impact, rather than purely theoretical research.

📈 Primary Responsibilities

• Spearhead the conceptualization, development, and iterative refinement of novel GenAI-powered video authoring tools, ensuring they meet production-level quality standards.

• Design and implement robust architectures for integrating diverse AI models (text, image, audio, video), including both internal Adobe models and external APIs, to create sophisticated agentic workflows.

• Develop intuitive multimodal editing and conversational interfaces that enable seamless interaction with complex generative AI systems for creative tasks.

• Conduct user studies and gather feedback directly from filmmakers and creative professionals, utilizing insights to rapidly iterate on prototypes and validate technical approaches.

• Proactively research and identify emerging trends in controllable generation, multimodal AI, and agentic systems to inform and shape the team's technical strategy and future innovation.

• Establish and promote best practices for bridging the gap between research experimentation and productization, mentoring junior engineers and fostering a scalable development environment.

• Contribute to the technical roadmap of Adobe's GenAI product strategy, influencing the direction of Creative Cloud and new AI-first product initiatives.

📝 Enhancement Note: The core responsibilities highlight a blend of deep technical expertise in GenAI and practical application. The emphasis on "production-level rigor," "agentic workflows," and "multimodal editing" points to a need for engineers who can not only understand the theoretical underpinnings of GenAI but also engineer complex systems that are reliable, scalable, and user-friendly for creative professionals. The close collaboration with end-users suggests a strong focus on user-centric design and iterative development cycles.

🎓 Skills & Qualifications

Education:

Experience:

• Minimum of 5 years of relevant industry experience in a senior research engineer, applied scientist, or advanced R&D engineering role.

Required Skills:

• Generative AI Expertise: Deep hands-on experience with state-of-the-art Generative AI models across multiple modalities, including text, image, audio, and video generation.

• Agentic Workflows & Controllable Generation: Profound understanding and practical application of agentic workflow principles and techniques for controllable AI generation.

• Rapid Prototyping & Iterative Development: Proven ability to quickly build, test, and refine functional prototypes for user-facing applications with a focus on production-level quality.

• System Architecture & Integration: Experience in designing and integrating complex systems, including connecting heterogeneous AI models (in-house and third-party) into cohesive workflows.

• Multimodal Systems: Strong background in developing and working with multimodal AI systems, particularly those involving creative content generation.

• User Collaboration & Feedback: Demonstrated ability to collaborate effectively with end-users (filmmakers, creative professionals) and incorporate their feedback into product development cycles.

• Research-to-Productization: Proven track record of translating ambiguous research ideas into impactful, demonstrable prototypes and contributing to productization efforts.

Preferred Skills:

• Ph.D. in a relevant field: A Ph.D. often signifies a deeper level of research contribution and specialized knowledge.

• Top-Tier Publications: A history of publications in leading AI/ML conferences (e.g., NeurIPS, ICML, CVPR, ICCV, SIGGRAPH) and journals demonstrates significant research impact.

• Creative Tool Development: Prior experience building tools specifically for creative professionals or within the media/entertainment industry.

• Conversational AI & Interfaces: Experience in designing and implementing advanced conversational interfaces for complex applications.

• Large-Scale AI Systems: Experience with developing or deploying AI systems at scale.

📝 Enhancement Note: The requirements emphasize a strong research foundation coupled with practical engineering skills. The "5+ years of relevant industry experience" and "MS or higher" suggest a mid-to-senior level role. The "preferred qualifications" like top-tier publications and prior creative tool experience would strongly differentiate candidates, indicating a desire for individuals who are not only technically proficient but also recognized leaders in the GenAI research community and have a passion for creative technologies.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• GenAI Project Demonstrations: Showcase at least 2-3 significant projects involving Generative AI, ideally in video, image, or multimodal generation. Each project should clearly articulate the problem, the AI models/techniques used, the system architecture, and the outcome.

• Prototyping Examples: Include examples of rapid prototypes developed, highlighting the iterative process, user feedback integration, and how these prototypes evolved towards production-readiness.

• Agentic Workflow Implementations: Detail any experience designing or implementing agentic workflows, demonstrating how multiple AI components were orchestrated to achieve a complex task.

• System Integration Case Studies: Provide examples of integrating diverse models or systems, illustrating your ability to connect heterogeneous components into a cohesive and functional system.

• User-Centric Development: Showcase instances where user feedback was actively incorporated into the development cycle, demonstrating a commitment to user-centric design in AI product development.

Process Documentation:

• Workflow Design & Optimization: Provide documentation or case studies that illustrate your approach to designing and optimizing complex workflows, particularly those involving AI pipelines and user interactions.

• Research-to-Productization Process: Describe your methodology for taking research concepts from ideation through prototyping to a demonstrable, product-ready state.

• User Study & Evaluation Methods: Outline your approach to conducting user studies, gathering qualitative and quantitative feedback, and using this data to drive technical and design decisions in AI product development.

• System Architecture Documentation: Present examples of system architecture diagrams or documentation that illustrate the design of complex AI systems, including model integration and data flow.

📝 Enhancement Note: For a role this focused on applied research and prototyping, a robust portfolio is critical. Candidates should be prepared to demonstrate not just theoretical knowledge but also tangible results. The emphasis on "production-level rigor" means portfolios should highlight projects that are well-documented, technically sound, and have clear, measurable outcomes or demonstrable user value. Case studies on integrating disparate AI models into coherent agentic workflows will be particularly important.

💵 Compensation & Benefits

Salary Range:

• Estimated Range: $204,800 - $296,600 annually (based on the provided Washington state pay range).

• Explanation: This range reflects the senior-level experience (5-10 years), specialized technical skills in Generative AI and research engineering, and the high cost of living in Seattle, Washington. Adobe's provided range for Washington is $204,800 - $296,600. The broader U.S. range of $164,600 - $313,300 indicates potential variation based on specific market adjustments within the US.

Benefits:

• Comprehensive Health Insurance: Medical, dental, and vision coverage for employees and their dependents.

• Retirement Savings Plan: 401(k) plan with company matching contributions.

• Paid Time Off: Generous vacation days, sick leave, and paid holidays.

• Stock Options/Equity: Potential for new hire equity awards, offering participation in company growth.

• Professional Development: Support for continuous learning, including access to training, conferences, and potentially tuition reimbursement.

• Employee Assistance Program (EAP): Confidential support services for personal and work-related challenges.

• Wellness Programs: Initiatives focused on employee well-being, including fitness resources and mental health support.

• Creative Tool Access: Potential access to Adobe's suite of creative tools for personal and professional use.

Working Hours:

• Standard full-time workweek, typically 40 hours.

• While on-site, Adobe often offers some flexibility in daily schedules, allowing for work-life balance, though core collaboration hours will be expected.

📝 Enhancement Note: The salary range provided for Washington state ($204,800 - $296,600) is specific and should be the primary reference for candidates in that location. The broader US range ($164,600 - $313,300) is also provided. The benefits listed are typical for large tech companies like Adobe and are tailored to attract and retain senior engineering talent. The mention of "new hire equity award" is a significant perk for senior roles.

🎯 Team & Company Context

🏢 Company Culture

Industry: Software & Technology, specializing in Creative Software, Digital Media, and AI.

Company Size: Large Enterprise (30,000+ employees worldwide). This implies a structured environment with established processes, significant resources, and opportunities for large-scale impact, but also potentially more layers of management and decision-making.

Founded: 1982. A long history in the creative industry provides a deep understanding of user needs and a strong foundation for innovation.

Team Structure:

• Generative Prototyping Team: A specialized research and development group focused on pioneering GenAI video authoring.

• Reporting Structure: Likely reports within Adobe Research, with potential close ties to product divisions like Creative Cloud or emerging AI product teams.

• Cross-functional Collaboration: Significant collaboration expected with filmmakers, creative professionals, product managers, and other AI/ML research teams within Adobe.

Methodology:

• Data-Driven Innovation: Emphasis on using data from user studies and performance metrics to guide development and product decisions.

• Agile & Iterative Development: Tight feedback loops with users for rapid prototyping and continuous improvement.

• Research & Application Blend: A culture that values both cutting-edge theoretical research and its practical application into tangible, user-facing tools.

• AI-First Mindset: Integrating AI and GenAI capabilities at the core of new product development and existing workflows.

Company Website: https://www.adobe.com/

📝 Enhancement Note: Adobe's culture is generally known for fostering creativity and innovation, with a strong emphasis on "Adobe for All" and diversity. As a large tech company, it offers stability and resources, while research teams like this one often operate with a degree of autonomy to explore frontier technologies. The "Generative Prototyping" team specifically is positioned at the forefront of Adobe's AI initiatives, suggesting a dynamic and fast-paced environment where impactful contributions are highly valued.

📈 Career & Growth Analysis

Operations Career Level: Senior Individual Contributor (Research Engineer). This role is distinct from traditional "Revenue Operations" or "Sales Operations" but is a critical "operations" role within the R&D function, focused on the operationalization of AI research into functional prototypes and future products. The "Senior" title implies leadership in technical execution, mentorship, and strategic input.

Reporting Structure: Reports to a Research Manager or Lead within Adobe Research. Collaboration will be broad, involving product teams and end-users, but the core technical direction will likely be guided by research leadership.

Operations Impact: This role has a direct impact on Adobe's strategic position in the rapidly evolving Generative AI market, particularly for creative professionals. Success means accelerating the development of next-generation creative tools, influencing product roadmaps, and potentially creating entirely new product categories. The "operations" here refers to the operationalization of AI research into usable technology.

Growth Opportunities:

• Technical Specialization: Deepen expertise in advanced GenAI techniques, multimodal systems, and agentic AI development.

• Leadership in R&D: Transition into a technical lead or architect role for larger research initiatives or product-focused R&D projects.

• Mentorship: Guide and mentor junior engineers and researchers, helping to build the team's capabilities.

• Cross-Functional Leadership: Lead initiatives that bridge research and product development, influencing product strategy and roadmaps.

• Industry Recognition: Opportunity to publish research, present at conferences, and build a personal reputation as a leader in GenAI for creative applications.

• Product Impact: See research efforts directly contribute to widely used Adobe products, impacting millions of creators globally.

📝 Enhancement Note: While not a traditional GTM operations role, this "Research Engineer" role involves significant "operationalization" of research into working systems. The career path emphasizes deep technical mastery and leadership within R&D, with clear opportunities to influence product strategy and gain industry recognition. The "operations" aspect is about making cutting-edge AI research function reliably and effectively for end-users.

🌐 Work Environment

Office Type: On-site (Seattle, WA). This indicates a collaborative, in-person work environment.

Office Location(s): Seattle, Washington, United States. This location offers access to a vibrant tech talent pool and a strong ecosystem for AI and software development.

Workspace Context:

• Collaborative Spaces: Adobe offices typically feature modern, open-plan workspaces designed to foster collaboration, with dedicated meeting rooms and project areas.

• Advanced Technology: Access to high-performance computing resources, specialized hardware (e.g., GPUs), and the full suite of Adobe's software development tools and creative applications.

• Team Interaction: Regular opportunities for informal and formal interaction with team members, research peers, product stakeholders, and creative professionals.

• Research-Focused Environment: A culture that encourages experimentation, knowledge sharing, and exploration of new ideas.

Work Schedule:

• Standard full-time schedule (approx. 40 hours/week).

• While on-site, Adobe aims for a balance between structured work and flexibility, allowing engineers to manage their time effectively to meet project deadlines and personal needs. Core hours for team collaboration will be essential.

📝 Enhancement Note: The on-site requirement in Seattle suggests a preference for a highly collaborative and interactive research environment, which is often crucial for rapid prototyping and user feedback loops in cutting-edge R&D. Access to specific hardware and software resources will be key for this role.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: HR or recruiter call to assess basic qualifications, interest, and cultural fit.

• Technical Phone Screen/Video Call: Deep dive into technical skills, focusing on GenAI, ML, prototyping, and system design. May involve coding challenges or conceptual problem-solving.

• On-site/Virtual On-site Interviews:

  • Research Deep Dive: Presentation of past research/projects from your portfolio, with in-depth Q&A on methodology, challenges, and impact.
  • System Design/Architecture: Problem-solving session focused on designing an agentic workflow or multimodal system for a given creative use case.
  • Coding/Prototyping Challenge: Practical exercise to assess coding proficiency, rapid prototyping skills, and approach to implementing AI features.
  • Behavioral & Team Fit: Interviews with potential team members and hiring manager to assess collaboration, communication, problem-solving approach, and alignment with Adobe's culture.

• Hiring Manager Interview: Final discussion to align on role expectations, career growth, and confirm mutual fit.

Portfolio Review Tips:

• Curate Strategically: Select 3-5 of your most impactful projects that directly align with the job description's requirements (GenAI, video, prototyping, agentic workflows, multimodal systems).

• Structure for Clarity: For each project, clearly outline:

  • Problem: The creative or technical challenge addressed.
  • Solution: The AI models, techniques, and system architecture employed.
  • Your Role: Specific contributions and responsibilities.
  • Impact/Outcome: Quantifiable results, user feedback, or product influence.
  • Tools & Tech: Key technologies and frameworks used.

• Highlight Production Rigor: Emphasize aspects of your projects that demonstrate production-level quality, scalability considerations, and robust engineering practices.

• Showcase Iteration: If possible, demonstrate how prototypes evolved based on feedback or experimentation.

• Prepare for Deep Dives: Be ready to discuss technical details, challenges encountered, trade-offs made, and lessons learned for each project.

Challenge Preparation:

• GenAI Fundamentals: Refresh knowledge on core GenAI concepts, different model architectures (transformers, diffusion models, etc.), and their applications.

• Multimodal AI Concepts: Understand how to combine information from different modalities (text, image, audio, video).

• Agentic Systems: Review principles of designing intelligent agents that can plan, reason, and act.

• System Design: Practice designing scalable systems for AI applications, considering data pipelines, model serving, and user interfaces.

• Coding Proficiency: Sharpen skills in Python and relevant ML/AI libraries (e.g., PyTorch, TensorFlow, Hugging Face).

• Problem-Solving: Practice breaking down complex, ambiguous problems into manageable components and proposing practical solutions.

• Adobe Context: Research Adobe's current GenAI initiatives (e.g., Firefly) and understand their product ecosystem (Creative Cloud).

📝 Enhancement Note: The interview process is typical for senior R&D roles in major tech companies, emphasizing both deep technical expertise and practical application. The portfolio is paramount; candidates must be able to clearly articulate their contributions and the impact of their work using concrete examples. The "production-level rigor" aspect suggests that simply having a research paper is not enough; candidates must show they can build working, high-quality systems.

🛠 Tools & Technology Stack

Primary Tools:

• Generative AI Frameworks: PyTorch, TensorFlow, JAX.

• ML Libraries: Hugging Face Transformers, Diffusers, Scikit-learn.

• Programming Languages: Python (primary), potentially C++ for performance-critical components.

• Prototyping Tools: Python-based frameworks, potentially web frameworks (e.g., Flask, FastAPI) for API development.

• Version Control: Git, GitHub/GitLab/Bitbucket.

Analytics & Reporting:

• Experimentation Platforms: Tools for tracking ML experiments (e.g., MLflow, Weights & Biases).

• Data Analysis Libraries: Pandas, NumPy.

• Visualization Tools: Matplotlib, Seaborn, potentially Tableau or Power BI for higher-level dashboards.

CRM & Automation:

• Not directly applicable to this research role, but understanding how research prototypes integrate into product ecosystems (e.g., via APIs) is relevant.

• Cloud Platforms: AWS, GCP, or Azure for model training and deployment.

📝 Enhancement Note: The technology stack is heavily focused on cutting-edge AI/ML development. Proficiency in Python and major deep learning frameworks is non-negotiable. Experience with cloud platforms for training and deploying models is also essential. The "heterogeneous models" requirement implies familiarity with integrating various types of AI components, likely through APIs or custom connectors.

👥 Team Culture & Values

Operations Values (Adobe's core values applied to this team):

• Excellence & Innovation: Driving forward the boundaries of GenAI for creative applications, seeking innovative solutions with high technical quality.

• Customer Focus: Deeply understanding and collaborating with filmmakers and creators to build tools that genuinely empower them.

• Collaboration & Inclusion: Working effectively across diverse teams, sharing knowledge, and valuing all contributions to foster a creative and productive environment.

• Integrity & Accountability: Taking ownership of research efforts, delivering on commitments, and maintaining high ethical standards in AI development.

• Empowerment: Encouraging individual initiative, providing opportunities for growth, and enabling team members to make significant contributions.

Collaboration Style:

• Highly Collaborative: Frequent interaction within the immediate team, with researchers across Adobe, and with external creative professionals.

• Feedback-Driven: An open culture where constructive feedback is exchanged regularly to refine prototypes and research directions.

• Knowledge Sharing: Encouraging presentations, internal tech talks, and documentation to disseminate learnings across the team and wider organization.

• Experimental & Agile: A willingness to experiment, learn from failures, and adapt quickly based on new findings and user input.

📝 Enhancement Note: Adobe's general company values of innovation, customer focus, and inclusion are expected to be paramount within this specialized research team. The collaborative style is crucial given the nature of rapid prototyping and direct user engagement. The team likely fosters an environment where pushing creative and technical boundaries is encouraged, balanced with a pragmatic approach to building functional tools.

⚡ Challenges & Growth Opportunities

Challenges:

• Rapidly Evolving Field: Keeping pace with the breakneck speed of advancements in Generative AI and multimodal systems requires continuous learning and adaptation.

• Bridging Research and Production: Translating bleeding-edge research into stable, scalable, and user-friendly production tools is a significant engineering challenge.

• Ambiguity in Creative Needs: Translating subjective and often ambiguous creative requirements from filmmakers into concrete technical specifications and functional prototypes.

• Model Integration Complexity: Architecting and maintaining cohesive workflows from diverse, potentially disparate, in-house and third-party AI models.

• Ethical AI Considerations: Navigating the ethical implications of generative AI, including bias, fairness, and responsible use, especially in creative contexts.

Learning & Development Opportunities:

• Cutting-Edge Research: Direct involvement with state-of-the-art GenAI research and development.

• Skill Deepening: Opportunities to gain deep expertise in specific areas of multimodal AI, agentic systems, and video generation.

• Industry Conferences: Potential to attend and present at leading AI/ML and creative technology conferences.

• Mentorship Programs: Access to senior researchers and engineers for guidance and career development.

• Product Impact: The chance to shape the future of creative tools used by millions worldwide.

• Internal Training & Workshops: Adobe likely offers extensive internal resources for skill development.

📝 Enhancement Note: The challenges are inherent to pioneering work in a fast-moving field like GenAI for creative applications. The growth opportunities are significant, offering deep technical specialization, leadership potential, and the chance to make a tangible impact on industry-leading products.

💡 Interview Preparation

Strategy Questions:

• GenAI Strategy: "How would you approach building a controllable text-to-video generation system that allows filmmakers fine-grained control over style, narrative, and characters?" (Prepare to discuss model choices, control mechanisms, workflow design, and evaluation metrics.)

• Agentic Workflow Design: "Describe how you would design an agentic workflow to assist a filmmaker in storyboarding, leveraging text-to-image, text-to-video, and conversational AI components." (Focus on orchestration, inter-model communication, user interaction, and feedback loops.)

• Research-to-Productization: "Walk us through your process for taking a novel GenAI research idea from initial concept to a functional prototype that can be tested with end-users." (Emphasize iterative development, user feedback integration, and quality considerations.)

Company & Culture Questions:

• Adobe's AI Vision: "What are your thoughts on Adobe's current GenAI initiatives like Firefly, and how do you see this role contributing to Adobe's broader AI strategy?" (Research Adobe Firefly and other AI products. Connect your skills and experience to their goals.)

• Collaboration Style: "Describe a time you collaborated with creative professionals or end-users to develop a new tool or feature. What challenges did you face, and how did you overcome them?" (Prepare examples demonstrating user empathy, communication, and iterative design based on feedback.)

• Measuring Impact: "How do you measure the success of a research prototype, especially one intended for creative applications?" (Discuss both technical metrics and user-centric evaluation methods, like user studies and adoption rates.)

Portfolio Presentation Strategy:

• Storytelling: Frame each project as a narrative: the problem, your innovative solution, the challenges, and the impactful outcome.

• Visuals: Use clear, compelling visuals (diagrams, screenshots, short video clips) to illustrate your work. For video-related projects, demonstrate the output.

• Technical Depth: Be prepared to explain the technical intricacies without getting lost in jargon. Tailor your explanation to the interviewer's presumed technical background.

• Quantify Impact: Whenever possible, use data and metrics to demonstrate the value and success of your projects (e.g., efficiency gains, user satisfaction scores, adoption rates).

• Focus on Your Role: Clearly articulate your specific contributions and leadership within each project, especially if it was a team effort.

Challenge Preparation:

• Coding: Practice Python coding challenges, focusing on data manipulation, algorithm implementation, and API interaction.

• System Design: Prepare to whiteboard or discuss system architectures for AI applications, considering scalability, performance, and integration.

• ML/GenAI Concepts: Be ready to explain core concepts and answer questions about model behavior, limitations, and potential biases.

📝 Enhancement Note: Interview preparation should focus on demonstrating a blend of deep technical expertise in GenAI and a user-centric, product-oriented mindset. Candidates need to articulate not just what they built, but why, how, and what the impact was, with a clear emphasis on the "production-level rigor" and user collaboration aspects of the role.

📌 Application Steps

To apply for this Senior Research Engineer position:

• Submit your application through the Adobe Careers portal using the provided link.

• Tailor your resume: Highlight your experience with Generative AI, multimodal systems, rapid prototyping, agentic workflows, and any experience with creative tools or user-facing applications. Quantify achievements where possible.

• Curate your portfolio: Select your strongest projects that best demonstrate your qualifications for this specific role. Ensure clear documentation of your process, technical approach, and impact. Prepare to present your portfolio effectively.

• Research Adobe's GenAI efforts: Familiarize yourself with Adobe Firefly, their AI strategy, and the Creative Cloud ecosystem. Understand how this role fits into their broader vision.

• Prepare for technical interviews: Review core GenAI concepts, system design principles, and practice coding challenges. Be ready to discuss your portfolio projects in detail.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.