Agentic AI Scientist - Forward-Deployed (Remote)

DigitalOcean; San Francisco (CA); Remote; USD 167,000 - 209,000; Full time

Job summary

DigitalOcean is seeking an Agentic AI Applied Scientist for the Forward Deployed Engineering team. You will embed with startups and AI-native customers, designing, deploying, and optimizing autonomous agent architectures on our high-performance AI infrastructure.

You will prototype new agentic workflows, integrate with client teams, and drive platform improvements with tangible customer impact. The role emphasizes production-grade Python, memory models, and tool-calling pipelines, with travel up

Qualifications

• 6+ years of hands-on experience in Applied AI, ML, or Data Science.
• Expertise in multi-agent frameworks and tool-use pipelines.
• Production Python & systems engineering experience including PyTorch stack.
• Customer-facing engineering mindset with strong technical communication.

Responsibilities

• Architect production-ready agentic frameworks and multi-agent workflows.
• Direct customer integration with CTOs and AI leads to translate requirements into deterministic AI workflows.
• Prototype and harden agentic frameworks for enterprise-scale production and memory retention.
• Develop evaluation frameworks and guardrails to monitor precision, latency, and cost across systems.
• Act as a feedback link between customers and AI/product teams to drive platform improvements.
• Travel up to 30% for engagements and collaborations.

Skills

Applied AI

Multi-agent frameworks

Python engineering

Technical communication

Education

Ph.D./Master’s in CS/ML/AI

Tools

LangGraph

AutoGen

Pydantic

FastAPI

Asyncio

PyTorch

Job description

DigitalOcean is seeking an Agentic AI Applied Scientist for the Forward Deployed Engineering team. You will embed with startups and AI-native customers, designing, deploying, and optimizing autonomous agent architectures on our high-performance AI infrastructure.

You will prototype new agentic workflows, integrate with client teams, and drive platform improvements with tangible customer impact. The role emphasizes production-grade Python, memory models, and tool-calling pipelines, with travel up