Staff Software Engineer, Applied Research

DigitalOcean•Seattle, WA

7d•Hybrid

About The Position

Dive in and do the best work of your career at DigitalOcean. Journey alongside a strong community of top talent who are relentless in their drive to build the simplest scalable cloud. If you have a growth mindset, naturally like to think big and bold, and are energized by the fast-paced environment of a true industry disruptor, you’ll find your place here. We value winning together—while learning, having fun, and making a profound difference for the dreamers and builders in the world. 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.

Requirements

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

Nice To Haves

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.
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.

Responsibilities

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.