Job description

Senior Machine Learning Engineer Agent Platform — Adobe Experience Platform THE OPPORTUNITY Build the Agent Platform That Powers Enterprise AI Adobe Experience Platform powers personalized experiences for the world s largest brands . Our AI team is building a production-grade platform for autonomous AI agents — not a wrapper around an LLM API, but a full agent runtime with sub-second orchestration, tool and skill layers spanning thousands of endpoints, long-term memory, sandboxed execution, and a multi-tenant Agent-Ops stack, all runtime-swappable across providers . We re hiring Senior ML Engineers to own major components end-to-end. You ll work at the intersection of applied ML and systems engineering, and your decisions will shape a system that serves Fortune 500 marketing teams. Multiple roles, one team: The team is growing fast to match the scale of what we’re building — we’re actively hiring multiple Senior ML Engineers across the focus areas described below. We’ll match your strengths to the right domain during the interview process. WHAT YOU’LL DO Your Impact You ll spend most of your time building platform infrastructure, with regular exposure to customer needs that shapes what you build. Build core agent infrastructure. Own major components of the platform — the agent runtime, tool execution layer, memory systems, sandboxed execution, or control plane — and ship production-ready code against real constraints: sub-second orchestration latency, cost-aware model routing, and high-throughput inference pipelines. Design ML workflows at enterprise scale. Build the systems for model customization, serving, and lifecycle management that let the platform adapt to diverse customer workloads. Innovate, don t just build. You ll have room to exp lore new approaches to agent reason ing, tool orchestration, memory, or evaluation — and carry the best ideas from experiment to production. We value engineers who push the platform forward with original thinking, not just execute on a spec. Close the loop with customers. Join regula r customer engagements to see how your systems perform in real deployments, then feed those insights back into the platform roadmap. This isn t a customer-facing role, but your work is directly shaped by the people who use it. Own what you ship. Architecture through production operations — deployment, monitoring, observability, and incident response. No throwing code over the wall. Example Focus Areas Most engineers go deep in one or two areas while collaborating across the broader platform: Agent runtime and orchestration Tool execution infrastructure Retrieval, memory, and context systems Evaluation, observability, and agent quality Sandboxed execution and security Fine-tuning, serving, and model lifecycle infrastructure WHAT YOU BRING Core Requirements Ph.D. or M.S. in Computer Science or related field required . 5 years of experience building and deploying production ML systems, with demonstrated work on models and AI-powered applications that serve real users at scale. Strong software engineering fundamentals: proficiency in Python and/or Java, experience designing APIs and microservices, and comfort owning production systems end-to-end (deployment, monitoring, incident response). Deep hands-on experience with at least one modern deep learning framework ( PyTorch , TensorFlow, JAX). Production experience with LLMs: prompt/context engineering, working with LLM APIs, fine-tuning, or building LLM-powered applications. Experience with cloud platforms (AWS or Azure) and data infrastructure (Postgres, Redis, Elasticsearch, Snowflake, or similar). Self-motivated with strong communication skills and the ability to influence technical decisions in a collaborative, multi-functional environment. What Sets You Apart You don t need all of these — depth in one or two is what matters. We ll match you to the domain where your experience has the most impact. Agent or LLM infrastructure depth . You ve built agent loops, tool-use orchestration, RAG pipelines, long-term memory systems, or fine-tuning/serving infrastructure — not as a prototype, but in production systems handling real traffic. Platform-scale systems thinking . You ve designed catalog systems, plugin architectures, or intent routing that work across hundreds or thousands of endpoints, and you ve dealt with the messy reality of overlap resolution, versioning, and cost-aware routing at that scale. ML-Ops or Agent-Ops experience . You ve built eval frameworks, execution tracing, drift detection, guardrails, or HITL intervention systems — the operational backbone that makes autonomous AI trustworthy in production. Builder who innovates. You don t just implement — you ve prototyped novel approaches, run experiments, and improved systems in ways that weren t on the original roadmap. Publications or open-source contributions are a plus, not a requirement. Multiplier instincts . You ve mentored engineers, shaped a team s technical roadmap, or built internal tools and practices that made the people around you more effective. LEARN MORE ABOUT OUR TEAM Explore our research, blog posts, and product work: Team page: business.adobe.com Blog: “Show, Don’t Tell: Multimodal Answers with AI Assistant” Blog: “Raising the Bar for AI Assistant in Adobe Experience Platform” Blog: “AI Assistant Evaluation and Continual Improvement” 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 $151,800 -- $265,350 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 $183,300 - $265,350 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 There is no deadline to apply to this job posting because Adobe accepts applications for this role on an ongoing basis. The posting will remain open based on hiring needs and position availability. 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.

Requirements

Must-haves

Senior Machine Learning Engineer
Agent Platform — Adobe Experience Platform
Build the Agent Platform That Powers Enterprise AI
We re hiring Senior ML Engineers to own major components end-to-end. You ll work at the intersection of applied ML and systems engineering, and your decisions will shape a system that serves Fortune 500 marketing teams.
You ll spend most of your time building platform infrastructure, with regular exposure to customer needs that shapes what you build.
Design ML workflows at enterprise scale. Build the systems for model customization, serving, and lifecycle management that let the platform adapt to diverse customer workloads.
Own what you ship. Architecture through production operations — deployment, monitoring, observability, and incident response. No throwing code over the wall.
Example Focus Areas
Most engineers go deep in one or two areas while collaborating across the broader platform:
Agent runtime and orchestration
Tool execution infrastructure
Retrieval, memory, and context systems
Evaluation, observability, and agent quality
Sandboxed execution and security
Fine-tuning, serving, and model lifecycle infrastructure
Ph.D. or M.S. in Computer Science or related field required .
5 years of experience building and deploying production ML systems, with demonstrated work on models and AI-powered applications that serve real users at scale.
Strong software engineering fundamentals: proficiency in Python and/or Java, experience designing APIs and microservices, and comfort owning production systems end-to-end (deployment, monitoring, incident response).
Deep hands-on experience with at least one modern deep learning framework ( PyTorch , TensorFlow, JAX).
Production experience with LLMs: prompt/context engineering, working with LLM APIs, fine-tuning, or building LLM-powered applications.
Experience with cloud platforms (AWS or Azure) and data infrastructure (Postgres, Redis, Elasticsearch, Snowflake, or similar).
Self-motivated with strong communication skills and the ability to influence technical decisions in a collaborative, multi-functional environment.
What Sets You Apart
You don t need all of these — depth in one or two is what matters. We ll match you to the domain where your experience has the most impact.

Nice-to-haves

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Application prep

Use the role’s language and application details while tailoring your materials.

Keywords to mirror

machine learningaillmapimlgopythonjavaawsazurepostgres

Platform: workday
Effort: long