Forward Deployed Applied Scientist II (Agentic AI)

DigitalOcean•Austin, TX

1d•Remote

About The Position

The Forward Deployed Engineering team works at the intersection of AI research, production deployment, and customer impact. The Forward Deployed Applied Scientist embeds with high-growth startups, technology innovators, and AI-native enterprise partners to design, build, and deploy custom autonomous agent architectures on DigitalOcean’s AI infrastructure. This remote role combines applied AI research, customer-facing engineering, and production platform validation, with travel of up to 30% for customer engagements, strategic workshops, conferences, and internal collaboration. The role entails designing production-ready multi-agent workflows and tool-augmented LLM architectures; translating ambiguous business requirements into reliable AI workflows with customers; prototyping emerging agentic frameworks and hardening them for enterprise production; building evaluation frameworks and safety guardrails; and sharing customer feedback with DigitalOcean product, engineering, and research teams to improve platform capabilities. The position also validates DigitalOcean’s AI-native platform, including inference, runtimes, orchestration, GPU platforms, and deployment workflows.

Requirements

At least 6 years of hands-on experience in Applied AI, Machine Learning, or Data Science.
Proven experience building multi-agent orchestration engines and expertise in tool-use or function-calling pipelines, MCP, structured outputs, dynamic planning, and persistent memory models.
Strong production Python and systems engineering skills, including Pydantic, FastAPI, Asyncio, and PyTorch.
Strong customer-facing engineering skills, empathy, and clear technical communication, including the ability to explain complex AI trade-offs to engineering leads and executive sponsors.
A bias for action, ownership of the customer experience, and a bold, resourceful approach.

Nice To Haves

Ph.D. or Master’s degree in Computer Science, Machine Learning, AI, or a related technical field.
Experience with deep learning frameworks such as PyTorch or TensorFlow, distributed training tools, and agentic AI frameworks.
Understanding of transformer types and state space models.
Research experience, including publications or patents, and knowledge of current research in LLMs, VLMs, and agentic frameworks.
Experience translating complex business tasks into AI engineering solutions and collaborating directly with client teams such as CTOs and AI leads.
Comfort working in fast-moving environments and tuning model workloads for diverse accelerator architectures.
Experience collaborating with customers, model vendors, or ecosystem partners on benchmarking, optimization, fine-tuning, or launch readiness initiatives.

Responsibilities

Design and implement multi-agent workflows, autonomous reasoning loops, and tool-augmented LLM architectures for real-world customer challenges.
Work directly with technology innovators, CTOs, and AI engineering leads to turn ambiguous business requirements into high-performance, deterministic AI and agentic workflows.
Prototype agentic frameworks, including LangGraph, AutoGen, or custom execution graphs, and prepare them for enterprise-scale production and stateful memory retention.
Develop reusable evaluation frameworks and safety guardrails to monitor reasoning precision, execution security, latency, and cost efficiency.
Relay customer deployment friction to AI/ML product teams and help translate it into platform enhancements.
Validate DigitalOcean AI platform capabilities, including inference engines, runtimes, orchestration systems, GPU platforms, and deployment workflows.
Share operational insights, architectural gaps, and scaling bottlenecks with Product Engineering and Research teams.
Travel up to 30% for customer engagements, strategic workshops, conferences, and internal collaboration.