About this role
About the Role Adobe Experience Platform 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, shaping a system that serves Fortune 500 marketing teams. Multiple roles on one team, growing fast to match scale.
What You'll Do
- 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, including model customization, serving, and lifecycle management to let the platform adapt to diverse customer workloads.
- Innovate, don’t just build: explore new approaches to agent reasoning, tool orchestration, memory, or evaluation and carry the best ideas from experiment to production.
- Close the loop with customers: engage in regular customer deployments to observe performance and feed insights back into the platform roadmap (this isn’t a customer-facing role, but user feedback shapes the work).
- Own what you ship: architecture through production operations — deployment, monitoring, observability, and incident response.
- No throwing code over the wall.
What We're Looking For
- Ph.D. or M.S. in Computer Science or related field.
- 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: Python and/or Java, API and microservices design, and comfort owning production systems end-to-end (deployment, monitoring, incident response).
- Deep hands-on experience with at least one modern DL framework (PyTorch, TensorFlow, JAX).
- Production experience with LLMs: prompt/context engineering, 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.
Nice to Have
- Agent or LLM infrastructure depth.
- Platform-scale systems thinking.
- ML-Ops or Agent-Ops experience.
- Builder who innovates: prototyping novel approaches and taking experiments to production.
- Publications or open-source contributions are a plus, not a requirement.
- Multiplier instincts: mentoring, shaping roadmaps, and building internal tools that increase team effectiveness.
Compensation & Benefits
- Not disclosed in posting.