Forward Deployed Applied Scientist II (Agentic AI)

DigitalOcean•San Francisco, CA

1d•Remote

About The Position

DigitalOcean’s 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. The role involves turning ambiguous customer requirements into reliable, production-ready agentic AI workflows; prototyping and hardening agent frameworks for enterprise use; creating evaluation and safety systems; and sharing customer deployment insights with DigitalOcean’s Product, Engineering, and Research teams. The scientist also validates AI platform capabilities, including inference, runtimes, orchestration, GPU platforms, and deployment workflows. 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 working with tool-use or function-calling pipelines, MCP, structured outputs, dynamic planning, and persistent memory models.
Strong production Python and systems engineering skills, including experience with Pydantic, FastAPI, Asyncio, and PyTorch.
Strong customer-facing engineering skills, empathy, and clear technical communication, including the ability to explain AI trade-offs to engineering leads and executive sponsors.
A bias for action, ownership of the customer experience, and an ability to work in a fast-moving environment.
Ability to travel up to 30%.

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, 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.
Experience tuning model workloads for diverse accelerator architectures.
Experience working with customers, model vendors, or ecosystem partners on benchmarking, optimization, fine-tuning, or launch-readiness initiatives.

Responsibilities

Design and implement production-ready multi-agent workflows, autonomous reasoning loops, and tool-augmented LLM architectures for customer challenges.
Work directly with customers, including technology innovators, CTOs, and AI engineering leads, to translate business requirements into high-performance, deterministic AI workflows.
Prototype agentic frameworks using tools such as LangGraph, AutoGen, or custom execution graphs, and harden 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.
Share customer feedback with AI/ML Product teams and turn deployment challenges into platform improvements.
Validate DigitalOcean AI platform capabilities, including its inference engine, runtimes, orchestration systems, GPU platforms, and deployment workflows.
Report 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.