About this role
Summary: Designs and builds agentic frameworks, memory systems, and retrieval systems so agents can reason, learn, and act reliably. Turns research into production-ready systems and mentors engineers.
We are looking for a Senior Machine Learning Engineer to join the Document Product Group and build agentic frameworks from the ground up. You will design the memory and retrieval systems that let agents reason over long horizons, learn from past interactions, and take reliable action. This is a hands-on role for someone who pairs research with the engineering discipline to turn ideas into working systems.
If you want to shape the foundations of how agents remember, retrieve, plan, and act, and you enjoy the ambiguity of 0→1 work, this role is for you.
What You’ll Do
- Design and build agentic frameworks from scratch, including planning, tool use, orchestration, state management, and multi-agent coordination.
- Architect and implement memory systems for agents (short-term, long-term, episodic, and semantic), including write, consolidation, forgetting, and update strategies.
- Build and optimize retrieval systems, including hybrid search, embedding and reranking pipelines, knowledge graphs, and retrieval over structured and unstructured data.
- Turn research ideas and recent literature into rigorous prototypes, then partner with engineering teams to harden them for production.
- Define evaluation methodologies for agent, memory, and retrieval quality, including benchmarks, LLM-as-a-judge approaches, offline and online metrics, and regression testing.
- Build the data pipelines, backend services, and observability needed to operate agentic systems reliably at scale.
- Bring a 0→1 product mindset: shape ambiguous ideas into measurable customer impact, and make informed build-versus-adopt decisions.
- Collaborate with product, design, research, and engineering partners to integrate these capabilities into customer-facing features.
- Stay current with research in agents, memory, retrieval, reasoning, and prompt optimization, and share what you learn with the team.
- Mentor engineers and help raise the team’s technical bar.
What You Need to Succeed
Required Qualifications
- Master’s or Ph.D. in Computer Science, Machine Learning, or a related technical field, or equivalent research experience.
- Research background in areas such as information retrieval, NLP, representation learning, or agents and reasoning, demonstrated through publications, patents, open-source contributions, or equivalent applied research.
- Hands-on experience building memory and retrieval systems for LLM-based applications, going beyond basic RAG to include design tradeoffs, failure modes, and evaluation.
- Demonstrated experience designing and building agentic architectures from first principles (not only configuring existing frameworks), 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, including 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 ML, deep learning, and modern generative AI techniques.
- Solid grounding in data structures, algorithms, and distributed systems.
- Familiarity with cloud platforms (AWS, GCP, or Azure) for scalable deployment.
- Comfort using AI coding agents to accelerate development.
- Excellent written and verbal communication, including the ability to explain complex research to technical and non-technical audiences.
Preferred Qualifications
- Experience with vector databases, graph databases, and search infrastructure
- Experience with long-context strategies, context engineering, memory compression and summarization, and personalization. Contributions to open-source agent, memory, or retrieval projects.
- Experience with prompt optimization frameworks, fine-tuning, and embedding or reranker model training.
- Experience with LLM-as-a-judge, agent evaluation, and benchmark design.
- 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.
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](https://www.adobe.com/adobe-for-all.html#:~:text=and%20culture.-,Our%20company%20values.,-As%20we%20look), [focus on people, purpose and community](https://www.adobe.com/corporate-responsibility.html), [Adobe for All](https://www.adobe.com/diversity.html), comprehensive [benefits programs](https://benefits.adobe.com/), the [stories we tell](https://blog.adobe.com/), the [customers](https://blog.adobe.com/en/topics/customer-stories) 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](https://www.eeoc.gov/sites/default/files/2023-06/22-088_EEOC_KnowYourRights6.12ScreenRdr.pdf) 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.](https://www.adobe.com/content/dam/cc/en/careers/pdfs/executed-eeo.pdf)
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.
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](https://careers.adobe.com/us/en/ai-and-your-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 $172,500 - $306,625 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 $211,800 - $306,625
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
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.