Senior Applied Scientist AI Guardrails Platform

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 acrossimage 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 aSenior Applied Scientistto help architect and scale this platform while leading key applied ML initiatives. This role sits at the intersection ofmultimodal machine learning large-scale ML systems andproductionAI 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.

• Optimizesystems for latency throughput and cost efficiency under real-world constraints.

• Define reusable patterns that generalize across products and enterprise deployments.

• Identifyand address system-level bottlenecks to ensure scalable and reliable deployment.

Safety-Aware Data Systems

• Develop strategies toidentifyand 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.

WhatYoullDo

• 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 settinghigh standardsfor system design and execution.

• Shape the long-term technical strategy for building robust scalable and responsible generative AI systems at Adobe.

WhatYoullNeed to Succeed

Research & Technical Depth

• PhD or MS in Computer Science Machine Learning AI ora relatedfield.

• 8 years of experience building and deploying machine learning systems in production environments withdemonstratedend-to-end ownership.

• Strongexpertisein 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.

• Proficiencyin modern ML development ecosystems (e.g. PythonPyTorch) 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 tooperateinopen-endedproblem spacesandidentifyhigh-leverage opportunities.

AI-Accelerated Development

• ExperienceusingAI-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 communicationskills 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.

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