1 day ago
Base Salary
$173k - $307k/yr
Responsibilities
- Design and build agentic frameworks from scratch, including planning, tool use, orchestration, state management, and multi-agent coordination.
- Architect memory systems covering short-term, long-term, episodic, and semantic memory, including write, consolidation, forgetting, and update strategies.
- Build and optimize retrieval systems with hybrid search, embedding and reranking pipelines, knowledge graphs, and structured and unstructured data retrieval.
- Turn research ideas and literature into rigorous prototypes and partner with engineering teams to productionize them.
- Define evaluation methodologies for agents, memory, and retrieval using benchmarks, LLM-as-a-judge approaches, offline and online metrics, and regression testing.
- Build data pipelines, backend services, and observability capabilities for reliable operation of agentic systems at scale.
- Shape ambiguous 0→1 ideas into measurable customer impact and make build-versus-adopt decisions.
- Collaborate with product, design, research, and engineering teams to integrate capabilities into customer-facing features.
- Stay current on research in agents, memory, retrieval, reasoning, and prompt optimization.
- Mentor engineers and help raise the team’s technical bar.
Requirements
- Master’s or Ph.D. in Computer Science, Machine Learning, or a related technical field, or equivalent research experience.
- Research experience in information retrieval, NLP, representation learning, agents, or reasoning, demonstrated through publications, patents, open-source contributions, or equivalent applied research.
- Hands-on experience building memory and retrieval systems for LLM-based applications beyond basic RAG, including design tradeoffs, failure modes, and evaluation.
- Experience designing and building agentic architectures from first principles, including orchestration, planning, tool use, and multi-agent patterns.
- 10+ years of experience in machine learning engineering, applied research, or production ML systems.
- Strong Python engineering skills and experience with system design, clean architecture, testing, CI/CD, version control, and code review practices.
- Experience taking ML-powered features from 0→1 through production and ongoing iteration.
- Experience deploying and monitoring ML and GenAI systems in production.
- Strong foundation in classical machine learning, deep learning, and modern generative AI techniques.
- Solid grounding in data structures, algorithms, and distributed systems.
- Familiarity with AWS, GCP, or Azure for scalable deployment.
- Comfort using AI coding agents to accelerate development.
- Excellent written and verbal communication skills, including explaining complex research to technical and non-technical audiences.
- Preferred experience with vector databases, graph databases, and search infrastructure.
- Preferred experience with long-context strategies, context engineering, memory compression and summarization, personalization, and open-source agent, memory, or retrieval projects.
- Preferred experience with prompt optimization frameworks, fine-tuning, embedding or reranker model training, LLM-as-a-judge, agent evaluation, and benchmark design.
- Preferred experience with MLOps tooling, experiment tracking, model lifecycle management, and observability for LLM and agent workflows.
- Track record of mentoring engineers and influencing technical direction on high-visibility projects.
Benefits
- U.S. base pay range of $172,500–$306,625 annually, with a California range of $211,800–$306,625.
- Non-sales roles may be eligible for the Annual Incentive Plan and certain roles may receive a new-hire equity award.
- Adobe provides comprehensive benefits programs and employee-focused culture and community initiatives.
Tech Stack
Categories
About Adobe
Adobe builds software for creative work, document workflows, and customer experience used by individuals, teams, and enterprises. Its products include Creative Cloud (Photoshop, Illustrator, Premiere Pro), Acrobat/Document Cloud, and Experience Cloud for marketing and analytics, sold primarily via subscriptions. Founded in 1982 and headquartered in San Jose, California, Adobe is a public company traded on NASDAQ under the ticker ADBE.