Applied Research

Building AI agents that can remember a conversation is the easy part. Building agents that maintain useful, accurate, and trustworthy memory across long-running workflows is one of the hardest open problems in production AI today.

That’s what this team works on. As a Staff AI/ML Engineer on our Applied Research team, you’ll own the technical direction for agent memory in DigitalOcean’s agentic systems: how agents store context, retrieve relevant information, update beliefs, personalize experiences, and reason over past interactions.

This is a senior IC role with broad technical scope. You’ll set direction, run experiments at scale, and work cross-functionally with product managers, scientists, applied researchers, engineers, and designers to move memory research from prototype to shipped product capability.

Own the agent memory roadmap

• Define and execute the applied research agenda for memory-enabled agentic AI, including long-term context, retrieval, personalization, and state management.
• Translate user needs, product signals, and research findings into practical memory architectures that improve real-world workflows.
• Stay close to the research frontier on agent memory, retrieval systems, multimodal recall, belief revision, and long-running agents.

Build production memory systems

• Design and build memory architectures for agentic AI, including episodic memory, semantic memory, user context, and long-term recall.
• Build reliable systems that support memory decay, fact grounding, belief updates, context compaction, and retrieval across sessions.
• Develop evaluation frameworks that measure memory quality, groundedness, reasoning reliability, personalization quality, and user outcomes.

Provide technical leadership

• Set technical direction across architecture, modeling decisions, experimentation strategy, and production readiness — without requiring direct management authority.
• Partner closely with product, engineering, design, science, and research teams to move work from ambiguous research ideas to shipped capabilities.
• Communicate complex AI systems clearly to both technical and non-technical stakeholders.

We’re looking for engineers who have shipped real AI systems — not just prototyped them. You likely bring:

• 8+ years of experience building production AI/ML systems, LLM-powered products, agentic workflows, retrieval systems, personalization systems, or applied research systems at scale.
• Hands-on experience with memory and retrieval systems such as embeddings, semantic search, knowledge graphs, RAG, personalization, or long-term user context.
• Strong understanding of agentic AI: memory, planning, tool use, state management, instruction following, self-correction, and action execution.
• Strong software engineering in Python and at least one production systems language.
• The judgment to balance research quality, product impact, latency, reliability, cost, and maintainability — and communicate those tradeoffs clearly

Strong signal

• Experience building memory, retrieval, personalization, or long-context systems in production.
• Experience with agent evaluation, offline/online experiments, feedback loops, or user outcome measurement.
• Prior Staff, Senior Staff, Tech Lead, or equivalent senior IC experience.

Nice to have

• Master’s or PhD in CS, ML, AI, or a related field — or equivalent depth demonstrated through industry work.
• Experience with production ML infrastructure: model serving, observability, data pipelines, feature stores, or experimentation platforms.
• Research contributions via peer-reviewed publications, patents, open-source work, or demonstrated applied research impact in AI agents, memory systems, retrieval, personalization, or applied ML.

• $216,800 - $271,000

*This is a hybrid role

JR: 2026-7946

#LI-Hybrid