Forward Deployed Applied Scientist II (Agentic AI)

DigitalOcean•Seattle, WA

1d•Hybrid

About The Position

The Forward Deployed Applied Scientist II (Agentic AI) joins DigitalOcean’s Forward Deployed Engineering team, working at the intersection of AI research, production deployment, and customer impact. The role 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. The scientist develops production-ready multi-agent workflows and tool-augmented LLM systems, translates customer business requirements into reliable AI workflows, and prototypes and hardens agentic frameworks for enterprise production. The role also builds evaluation and safety systems, improves DigitalOcean’s AI platform through customer feedback, and validates platform capabilities as a first customer. Travel of up to 30% is required for customer engagements, workshops, conferences, and internal collaboration.

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 mindset, empathy, and technical communication skills, including the ability to explain AI trade-offs to engineering leads and executive sponsors.
Bias for action, ownership of the customer experience, and an ability to think big, bold, and scrappy.

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, such as 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, including 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.

Responsibilities

Design and implement multi-agent workflows, autonomous reasoning loops, and tool-augmented LLM architectures for 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 harden them for enterprise 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 convert it into platform enhancements.
Validate DigitalOcean AI platform capabilities, including inference engines, runtimes, orchestration systems, GPU platforms, and deployment workflows.
Surface operational insights, architectural gaps, and scaling bottlenecks to Product Engineering and Research teams.
Travel up to 30% for customer engagements, strategic workshops, conferences, and internal collaboration.