Staff Applied Scientist - AI Guardrails

Adobe · Enterprise · San Jose, CA +2

Staff Applied Scientist focused on architecting and scaling AI guardrails and safety systems for generative models across multimodal platforms (image, video, audio). This role involves data-centric safety, detection, feedback-driven improvements, and translating research into production-ready systems, with a strong emphasis on ML systems architecture and responsible AI deployment.

What you'd actually do

Lead the architectural development of scalable, data-centric safety and guardrail systems across multimodal ML platforms.
Drive applied ML initiatives spanning data-centric safety, detection, and feedback-driven system improvement.
Translate research advances into production-ready systems, balancing model quality, scalability, and efficiency.
Act as a technical lead and mentor, guiding applied scientists and engineers while setting high standards for system design and execution.
Shape the long-term technical strategy for building robust, scalable, and responsible generative AI systems at Adobe.

Skills

Required

PhD or MS in Computer Science, Machine Learning, AI, or a related field.
8+ years of experience building and deploying machine learning systems in production environments, with demonstrated end-to-end ownership.
Strong expertise in multimodal machine learning, including vision-language and generative models.
Experience working with large-scale datasets and applying data-centric approaches to improve system performance and robustness.
Proficiency in modern ML development ecosystems (e.g., Python, PyTorch), with the ability to translate research into scalable implementations.
Experience designing scalable ML systems under real-world production constraints.
Strong intuition for trade-offs between model quality, latency, and infrastructure cost.
Experience integrating ML systems into large-scale product environments.
Strong experimental design and evaluation skills.
Experience analyzing complex system-level failure modes.
Ability to operate in open-ended problem spaces and identify high-leverage opportunities.
Experience using AI-assisted development tools to accelerate experimentation and system design.
Ability to combine rapid iteration with production-quality implementation.
Experience working in multi-functional research-to-product environments.
Strong communication skills with the ability to influence technical direction across teams.

Nice to have

Experience with trust & safety systems, content moderation, or AI safety infrastructure.
Background in large-scale multimodal data processing or dataset curation.
Experience deploying ML systems serving large user bases.
Research contributions in machine learning systems, multimodal AI, or responsible AI.

What the JD emphasized

production AI safety infrastructure
Guardrails Platform Architecture
Safety-Aware Data Systems
identify and mitigate intellectual property and trust & safety risks
advance methods to improve coverage and robustness of safety systems
system-level thinking around efficiency, scalability, and reliability of data-centric safety mechanisms
Partner with modeling teams to ensure data quality and safety signals effectively translate into improved model behavior
Lead the architectural development of scalable, data-centric safety and guardrail systems across multimodal ML platforms
Drive applied ML initiatives spanning data-centric safety, detection, and feedback-driven system improvement
Translate research advances into production-ready systems, balancing model quality, scalability, and efficiency
8+ years of experience building and deploying machine learning systems in production environments, with demonstrated end-to-end ownership
Strong expertise in multimodal machine learning, including vision-language and generative models
Experience working with large-scale datasets and applying data-centric approaches to improve system performance and robustness
Experience designing scalable ML systems under real-world production constraints
Strong intuition for trade-offs between model quality, latency, and infrastructure cost
Experience integrating ML systems into large-scale product environments
Strong experimental design and evaluation skills
Experience analyzing complex system-level failure modes
Ability to operate in open-ended problem spaces and identify high-leverage opportunities
Experience using AI-assisted development tools to accelerate experimentation and system design
Ability to combine rapid iteration with production-quality implementation
Experience working in multi-functional research-to-product environments
Experience with trust & safety systems, content moderation, or AI safety infrastructure
Background in large-scale multimodal data processing or dataset curation
Experience deploying ML systems serving large user bases
Research contributions in machine learning systems, multimodal AI, or responsible AI

Other signals

architect and scale this platform
leading key applied ML initiatives
multimodal machine learning
large-scale ML systems
production AI safety infrastructure
guardrail systems to scale across models, products, and enterprise use cases
Safety-Aware Data Systems
identify and mitigate intellectual property and trust & safety risks
advance methods to improve coverage and robustness of safety systems
system-level thinking around efficiency, scalability, and reliability of data-centric safety mechanisms
Partner with modeling teams to ensure data quality and safety signals effectively translate into improved model behavior
Lead the architectural development of scalable, data-centric safety and guardrail systems across multimodal ML platforms
Drive applied ML initiatives spanning data-centric safety, detection, and feedback-driven system improvement
Translate research advances into production-ready systems, balancing model quality, scalability, and efficiency
Act as a technical lead and mentor
Shape the long-term technical strategy for building robust, scalable, and responsible generative AI systems at Adobe
8+ years of experience building and deploying machine learning systems in production environments, with demonstrated end-to-end ownership
Strong expertise in multimodal machine learning, including vision-language and generative models
Experience working with large-scale datasets and applying data-centric approaches to improve system performance and robustness
Proficiency in modern ML development ecosystems (e.g., Python, PyTorch), with the ability to translate research into scalable implementations
Experience designing scalable ML systems under real-world production constraints
Strong intuition for trade-offs between model quality, latency, and infrastructure cost
Experience integrating ML systems into large-scale product environments
Strong experimental design and evaluation skills
Experience analyzing complex system-level failure modes
Ability to operate in open-ended problem spaces and identify high-leverage opportunities
Experience using AI-assisted development tools to accelerate experimentation and system design
Ability to combine rapid iteration with production-quality implementation
Experience working in multi-functional research-to-product environments
Strong communication skills with the ability to influence technical direction across teams
Experience with trust & safety systems, content moderation, or AI safety infrastructure
Background in large-scale multimodal data processing or dataset curation
Experience deploying ML systems serving large user bases
Research contributions in machine learning systems, multimodal AI, or responsible AI

Read full job description

Generative AI is redefining creativity. Ensuring that these systems are safe, controllable, and respectful of intellectual property is one of the most important open challenges in deploying generative models responsibly at scale.

The Adobe Firefly Applied Science & Machine Learning team is building a unified guardrails platform that enables safe training and deployment of generative models across image, video, and audio. This includes large-scale automated data moderation systems, multimodal detection and safety mechanisms, and learning based on input that continuously improves system robustness.

We are seeking a Staff Applied Scientist to help architect and scale this platform while leading key applied ML initiatives. This role sits at the intersection of multimodal machine learning, large-scale ML systems, and production AI safety infrastructure. The ideal candidate will drive high-impact technical initiatives while defining the architectural patterns that enable guardrail systems to scale across models, products, and enterprise use cases.

Research Areas You Will Drive

Guardrails Platform Architecture

Architect end-to-end systems that integrate data pipelines, detection, evaluation, and model control mechanisms.
Optimize systems for latency, throughput, and cost efficiency under real-world constraints.
Define reusable patterns that generalize across products and enterprise deployments.
Identify and address system-level bottlenecks to ensure scalable and reliable deployment.

Safety-Aware Data Systems

Develop strategies to identify and mitigate intellectual property and trust & safety risks in large-scale datasets.
Advance methods to improve coverage and robustness of safety systems, particularly across long-tail and evolving concept spaces.
Drive system-level thinking around efficiency, scalability, and reliability of data-centric safety mechanisms.
Partner with modeling teams to ensure data quality and safety signals effectively translate into improved model behavior.

What You’ll Do

Lead the architectural development of scalable, data-centric safety and guardrail systems across multimodal ML platforms.
Drive applied ML initiatives spanning data-centric safety, detection, and feedback-driven system improvement.
Translate research advances into production-ready systems, balancing model quality, scalability, and efficiency.
Act as a technical lead and mentor, guiding applied scientists and engineers while setting high standards for system design and execution.
Shape the long-term technical strategy for building robust, scalable, and responsible generative AI systems at Adobe.

What You’ll Need to Succeed

Research & Technical Depth

PhD or MS in Computer Science, Machine Learning, AI, or a related field.
8+ years of experience building and deploying machine learning systems in production environments, with demonstrated end-to-end ownership.
Strong expertise in multimodal machine learning, including vision-language and generative models.
Experience working with large-scale datasets and applying data-centric approaches to improve system performance and robustness.
Proficiency in modern ML development ecosystems (e.g., Python, PyTorch), with the ability to translate research into scalable implementations.

ML Systems Architecture

Experience designing scalable ML systems under real-world production constraints.
Strong intuition for trade-offs between model quality, latency, and infrastructure cost.
Experience integrating ML systems into large-scale product environments.

Scientific & Systems Rigor

Strong experimental design and evaluation skills.
Experience analyzing complex system-level failure modes.
Ability to operate in open-ended problem spaces and identify high-leverage opportunities.

AI-Accelerated Development

Experience using AI-assisted development tools to accelerate experimentation and system design.
Ability to combine rapid iteration with production-quality implementation.

Collaborative Approach

Experience working in multi-functional research-to-product environments.
Strong communication skills with the ability to influence technical direction across teams.

Preferred Qualifications

Experience with trust & safety systems, content moderation, or AI safety infrastructure.
Background in large-scale multimodal data processing or dataset curation.
Experience deploying ML systems serving large user bases.
Research contributions in machine learning systems, multimodal AI, or responsible AI.

#FireflyGenAI

About Adobe

Adobe empowers everyone to create through innovative platforms and tools that unleash creativity, productivity and personalized customer experiences. Adobe’s industry-leading offerings including Adobe Acrobat Studio, Adobe Express, Adobe Firefly, Creative Cloud, Adobe Experience Platform, Adobe Experience Manager, and GenStudio enable people and businesses to turn ideas into impact, powered by AI and driven by human ingenuity.

Our 30,000+ employees worldwide are creating the future and raising the bar as we drive the next decade of growth. We’re on a mission to hire the very best and believe in creating a company culture where all employees are empowered to make an impact. At Adobe, we believe that great ideas can come from anywhere in the organization. The next big idea could be yours.

** Let’s Adobe together**

At Adobe, we believe in creating a company culture where all employees are empowered to make an impact. Learn more about Adobe life, including our values and culture, focus on people, purpose and community, Adobe for All, comprehensive benefits programs, the stories we tell, the customers we serve, and how you can help us advance our mission of empowering everyone to create.

Adobe is proud to be an Equal Employment Opportunity employer. We do not discriminate based on gender, race or color, ethnicity or national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status, or any other protected characteristic. Learn more.

Adobe aims to make our Careers website and recruiting process accessible to any and all users. If you have a disability or special need that requires accommodation to navigate our website or complete the application process, email accommodations@adobe.com or call +1 408-536-3015.

AI Use Guidelines for Interviews: Our interviews are designed to reflect your own skills and thinking. The use of AI or recording tools during live interviews is not permitted unless explicitly invited by the interviewer or approved in advance as part of a reasonable accommodation. If these tools are used inappropriately or in a way that misrepresents your work, your application may not move forward in the process.

At Adobe, we empower employees to innovate with AI — and we look for candidates eager to do the same. As part of the hiring experience, we provide clear guidance on where AI is encouraged during the process and where it’s restricted during live interviews. See how we think about AI in the hiring experience.

Expected Pay Range:

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 $164,000 -- $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 California, the pay range for this position is $216,400 - $313,300 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 incentives are in the form of sales commission plans. Non-sales roles starting salaries are expressed as base salary and short-term incentives are in the form of the Annual Incentive Plan (AIP).

In addition, certain roles may be eligible for long-term incentives in the form of a new hire equity award.

State-Specific Notices:

California:

Fair Chance Ordinances

Adobe will consider qualified applicants with arrest or conviction records for employment in accordance with state and local laws and “fair chance” ordinances.

Colorado:

Application Window Notice

Apr 30 2026 12:00 AM

If this role is open to hiring in Colorado (as listed on the job posting), the application window will remain open until at least the date and time stated above in Pacific Time, in compliance with Colorado pay transparency regulations. If this role does not have Colorado listed as a hiring location, no specific application window applies, and the posting may close at any time based on hiring needs.

Massachusetts:

Massachusetts Legal Notice

It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.