Senior Machine Learning Engineer

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 explore new approaches to agent reasoning, 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 regular 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: https://business.adobe.com/resources/adobe-experience-platform-ai.html

• 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"