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Applied Scientist 5
Adobe
Bengaluru
9+ years
Today
$90.4K–107.2K/yr
Full-time
Onsite
Skills Required
LLM
Gen AI
Multimodal Learning
NLP
Machine Learning
Deep Learning
Optimization
Statistical Modeling
Generative Modeling
Computer Vision
Representation Learning
Reinforcement Learning
Reasoning
Planning
Foundation Models
Description
Adobe is seeking a Senior Applied Scientist to lead foundational research in agentic AI and intelligent systems. The role is research-intensive, focused on advancing models and learning paradigms that enable AI agents to reason, plan, learn, remember, interact with tools, and handle long-horizon tasks.
Company: Adobe
Role: Senior Applied Scientist
Experience
- 9+ years of hands-on ML engineering experience in industry or research
- Strong fundamentals in machine learning, deep learning, optimization, and statistical modeling
- Demonstrated ability to formulate novel research problems, develop new approaches, and experimentally validate hypotheses
- Strong publication record and/or track record of delivering novel ML research with measurable impact
- Strong programming and experimentation skills with contemporary deep learning frameworks
- Excellent written and verbal communication skills with cross-functional stakeholders
Qualification
- MS or PhD in Computer Science, Machine Learning, Artificial Intelligence, or a related field
Responsibilities
- Conduct fundamental and applied research in agentic AI, foundation models, reasoning, planning, memory, tool use, multimodal intelligence, and long-horizon interaction
- Develop novel model architectures, learning algorithms, training methodologies, and inference techniques to improve AI agent capabilities
- Advance agent intelligence through research in LLMs, generative models, NLP, computer vision, multimodal learning, representation learning, and reinforcement learning
- Investigate reasoning, planning, memory, grounding, adaptation, self-improvement, tool use, and learning from interaction
- Build and evaluate research prototypes and establish rigorous experimental methodologies to validate new ideas
- Take research advances to product
- Stay current with rapidly evolving research in foundation models and agentic AI and identify opportunities for differentiated product capabilities
- Lead technical design reviews and write engineering RFCs
- Set quality standards for the team
- Mentor junior and mid-level ML engineers
- Collaborate with product, research, and infrastructure teams to translate research ideas into shipped features
More Skills agentic AI, intelligent systems, memory, tool use, long-horizon interaction, model architectures, learning algorithms, training methodologies, inference techniques, grounding, adaptation, self-improvement, learning from interaction, experimental methodologies, modern deep learning frameworks, engineering RFCs, code reviews, pair programming
Other
- This is not a prompt-engineering or agent orchestration role
- The ideal candidate should be able to develop novel modeling and learning approaches rather than primarily assembling existing LLMs, prompts, or agent frameworks
- Research advances may be submitted as IP and published at top-tier conferences
- Adobe describes itself as an employee base of 30,000+ worldwide and emphasizes a culture where great ideas can come from anywhere in the organization
- Adobe highlights its offerings including Adobe Acrobat Studio, Adobe Express, Adobe Firefly, Creative Cloud, Adobe Experience Platform, Adobe Experience Manager, and GenStudio
- Adobe states it is an Equal Employment Opportunity employer
- Adobe provides accessibility accommodations for candidates with disabilities or special needs
- AI use guidelines for interviews restrict unauthorized AI or recording tools during live interviews
Prepare for this role Recommended resources to build the skills for this position. Sponsored.
Generative AI with Large Language Models
Coursera
Comprehensive LLM course covering transformer architecture, fine-tuning, RLHF, and deployment.
Large Language Models: Application through Production edX
Production-focused LLM course covering deployment, monitoring, and scaling.
Deep Learning Specialization
Coursera
Five-course deep learning series covering CNNs, RNNs, transformers, and ML strategy.
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